The People Science Glossary: An Evidence Index for Modern Management

Every core people-science term, defined rigorously and pinned to its canonical source.

Midway through a talent-review meeting, someone finally asks the question everyone has been avoiding: what do we actually mean by “potential”? Nobody has the same answer, and the ratings on the wall suddenly look less like evidence and more like opinion with a grid around it. Most management vocabulary works this way — used constantly, defined rarely, sourced almost never.

This glossary is the fix: a maintained index of the terms modern people management runs on, each defined so it is true out of context, and each pinned to the primary research or authoritative framework it comes from. It is a companion to Cadence’s People Science research pillar, which develops the full argument behind these concepts; this page is the reference layer.

Key takeaways

1. Feedback improves performance on average, but more than one-third of feedback interventions in the foundational meta-analysis reduced performance — definitions of “feedback” that ignore this are incomplete. [1]
2. Organizational justice is not one thing: distributive, procedural, interpersonal, and informational justice are distinct dimensions, each independently predicting satisfaction, commitment, citizenship, and withdrawal. [8]
3. Psychological safety — a team’s shared belief that it is safe for interpersonal risk-taking — was defined and measured by Edmondson (1999) and is associated with team learning behavior and performance. [23]
4. Mechanical (statistical) combination of evidence equals or outperforms holistic clinical judgment on average across more than a century of prediction research. [24] [9]
5. Team engagement is consistently associated with productivity, profitability, retention, and customer satisfaction in the largest business-unit meta-analyses ever conducted. [5] [6]
6. Bundles of ability-, motivation-, and opportunity-enhancing HR practices relate to organizational performance more strongly than isolated practices — people practices work as systems. [25] [22]
7. Popular management ratios like 5:1 are heuristics with out-of-domain origins, not workplace laws — a distinction this glossary marks explicitly wherever it applies. [26] [11]

How to use this index

Terms are grouped into eight thematic clusters. Each entry gives a definition that stands on its own, a Canonical source line pointing to the numbered reference, and — where a Cadence module is relevant — an In Cadence note with the module’s current availability (live today, preview, or roadmap). Cadence’s own coinages and heuristics are labeled as exactly that. If you quote a definition, cite the canonical source for the science and this page for the wording.

Conversations & Feedback

The manager-employee conversation is where most management actually happens; the practitioner’s guide is How to Run Effective 1:1s.

Structured 1:1

A structured 1:1 is a recurring, agenda-driven meeting between a manager and a direct report with shared preparation, documented notes, and action items carried forward between sessions. Its evidence base is the mechanism it enables — task-relevant feedback and explicit prioritization — rather than the calendar invite itself: Microsoft’s Work Trend Index found 81% of employees said it was important for managers to help prioritize their workload, while fewer than a third said they had ever received clear one-on-one guidance. [13]

Canonical source: Kluger & DeNisi (1996) [1]; Microsoft Work Trend Index (2022) [13]. In Cadence: structured 1:1 workspace, agendas, and action items are live today; AI summaries and coaching are preview.

Feedback intervention theory (FIT)

Feedback intervention theory, proposed by Kluger and DeNisi (1996) alongside their meta-analysis of 607 effect sizes, holds that feedback’s effect depends on where it directs the recipient’s attention: feedback focused on task details tends to help, while feedback that draws attention to the self tends to hurt. Although the average effect of feedback was positive (d = 0.41), over one-third of feedback interventions reduced performance — the founding result behind “feedback design matters.”

Canonical source: Kluger & DeNisi (1996) [1].

Psychological safety

Psychological safety is a shared belief held by members of a team that the team is safe for interpersonal risk-taking — that one can ask questions, admit errors, and raise concerns without being punished or humiliated. Edmondson (1999) defined and operationalized the construct at the team level and found it associated with team learning behavior, which in turn related to team performance. It is a team-climate property, not an individual personality trait, and it is not the same as comfort or the absence of accountability.

Canonical source: Edmondson (1999) [23].

The 5:1 recognition heuristic

The 5:1 heuristic holds that documented positive interactions with an employee should outnumber corrective ones by roughly five to one. It is a heuristic, not a law: the ratio traces to Gottman and Levenson’s research on married couples — an out-of-domain finding [26] — and no peer-reviewed study establishes 5:1 as an optimal workplace ratio. What recognition research does support is timely, honest, individualized acknowledgment as a manager behavior tied to engagement. [11]

Canonical source: Gottman & Levenson (1992) [26] (origin, marital interaction); Gallup [11] (workplace recognition). In Cadence: recognition feed, badges, and comments are live today; 5:1 ratio tracking is preview, and Cadence labels the ratio a coaching heuristic, not proven causal math.

Goals & Performance

Goal-setting theory

Goal-setting theory, developed by Locke and Latham across four decades of studies, finds that specific, challenging goals produce higher performance than vague “do your best” instructions — provided goal commitment, ability, and feedback are present and task complexity is manageable. [2] Its documented failure modes are equally established: rigid, high-stakes targets can narrow focus, encourage short-termism and risk-taking, and in some conditions promote unethical behavior. [4]

Canonical source: Locke & Latham (2002) [2]; Ordóñez et al. (2009) [4] (boundary conditions). In Cadence: goal and OKR tracking, check-ins, and at-risk views are live today.

Learning goals vs. performance goals

A performance goal specifies an outcome level to attain; a learning goal specifies the acquisition of strategies, processes, or knowledge needed to master a task. Goal-setting research finds that when a task is novel or complex, assigning a specific learning goal often outperforms assigning a performance goal, because attention goes to discovering how to do the work rather than to an outcome the person cannot yet reach. [3]

Canonical source: Locke & Latham (2006) [3].

OKR (Objectives and Key Results)

OKR is a practitioner goal-management framework — developed at Intel under Andy Grove and popularized by John Doerr’s Measure What Matters — that pairs a qualitative objective with a small set of measurable key results used to judge progress. [27] OKRs are an implementation pattern, not a distinct scientific theory: they inherit both the evidence base and the boundary conditions of goal-setting theory, including the failure modes of ritualized or over-prescribed targets. [2]

Canonical source: What Matters, “OKRs Explained” [27] (framework definition); Locke & Latham (2002) [2] (underlying science). In Cadence: OKR tracking with goal tree and alignment views is live today.

Engagement & Listening

Managers dominate this signal — Gallup found they account for at least 70% of the variance in team engagement scores [7] — and the practitioner’s guide to listening without burning trust is Measuring Culture Without Survey Fatigue.

Employee engagement

Employee engagement is the involvement and enthusiasm employees feel toward their work and workplace, typically measured by survey at the business-unit level. The foundational meta-analysis (Harter, Schmidt, & Hayes, 2002; 7,939 business units across 36 companies) found generalizable relationships between engagement and outcomes including customer satisfaction, productivity, profit, turnover, and accidents [5]; Gallup’s continuing meta-analytic program replicates these associations at larger scale. [6] These are strong, replicated associations, not randomized causal proof.

Canonical source: Harter, Schmidt, & Hayes (2002) [5]; Gallup Q12 meta-analysis, 11th ed. [6].

Q12

The Q12 is Gallup’s proprietary 12-item employee engagement instrument, covering needs such as clear expectations, materials and equipment, recognition, development, and opportunities to do what one does best. [12] Its supporting evidence base is Gallup’s Q12 meta-analysis — now in its 11th edition, the largest study of its kind — relating team-level engagement to eleven performance outcomes. [6]

Canonical source: Gallup Q12 [12]; Gallup Q12 meta-analysis [6].

Pulse survey

A pulse survey is a short, frequent employee survey — typically a handful of items at weekly-to-quarterly intervals — designed to track sentiment trends between or instead of long annual censuses. [28] Pulse data is useful when it feeds a listening-and-action loop at the team level; frequent measurement without visible response accelerates survey fatigue rather than insight.

Canonical source: AIHR employee pulse surveys guide [28]. In Cadence: the survey engine, including pulse, is preview — not GA.

Fairness & Employee Relations

Organizational justice

Organizational justice is employees’ perception of fairness in the workplace, comprising four related but distinct dimensions: distributive justice (fairness of outcomes and allocations), procedural justice (fairness and consistency of decision processes), interpersonal justice (dignity and respect in how people are treated), and informational justice (honesty and adequacy of explanations). Colquitt and colleagues’ meta-analytic review of 25 years of research found each dimension independently predicts outcomes including job satisfaction, organizational commitment, citizenship behavior, withdrawal, and performance. [8]

Canonical source: Colquitt et al. (2001) [8].

Employee relations

Employee relations (ER) is the discipline of managing the employment relationship — both individual relationships between employer and employee and, where present, collective relationships through representatives — including casework on conduct, grievances, conflict, and workplace policy. [29] Justice research supplies its operating standard: ER processes are more trusted and defensible when procedures are consistent, explanations are honest, treatment is respectful, and follow-through is documented. [8]

Canonical source: CIPD Employee Relations factsheet [29]; Colquitt et al. (2001) [8]. In Cadence: ER case management with case notes and escalation tracking is live today; ER analytics are preview.

Talent & Calibration

The full practice discussion — including where the grid goes wrong — is in the 9-Box Talent Calibration Guide.

9-box calibration

The 9-box is a 3x3 talent-review grid plotting an employee’s current performance against assessed future potential, commonly used for succession planning and development prioritization. [15] It is a practitioner framework, not a validated psychometric instrument: without clear criteria, multiple perspectives, and trend evidence, it can harden subjective labels, reproduce bias, and manufacture false precision.

Canonical source: AIHR 9-box practitioner’s guide [15]. In Cadence: 9-box talent calibration is roadmap — Coming Q3, not live today.

Talent calibration

Talent calibration is a structured group process in which multiple raters align their evaluations of employees against shared criteria, surfacing and resolving rating disagreements before decisions are made. Its evidentiary rationale comes from research on structured evidence combination: consistent, criteria-anchored judgment outperforms unstructured holistic impressions [9] — while the same research warns that structure does not automatically make outputs fair or correct.

Canonical source: Kuncel et al. (2013) [9]; AIHR [15]. In Cadence: roadmap — part of the Coming Q3 calibration workflow.

Adverse impact & the four-fifths rule

Adverse impact is a substantially different rate of selection (hiring, promotion, or other employment decision) that disadvantages members of a protected group, even absent discriminatory intent. The four-fifths rule is the Uniform Guidelines’ enforcement rule of thumb: a protected group’s selection rate below 80% of the highest group’s rate is generally regarded as evidence of adverse impact, which triggers a burden to validate the selection procedure and consider less-impactful alternatives. [10] Talent-review outputs that feed employment decisions fall within this discipline’s reach.

Canonical source: EEOC, Uniform Guidelines framework [10].

Mechanical vs. clinical prediction

Mechanical (statistical, rule-based) prediction combines evidence using a consistent formula; clinical (holistic) prediction combines it in an expert’s head. Grove and colleagues’ meta-analysis of 136 studies found mechanical prediction equals or outperforms clinical judgment on average — roughly a 10% accuracy advantage — across clinical and human-prediction domains [24], and Kuncel and colleagues replicated the pattern specifically in selection and admissions decisions. [9] The finding supports consistent, structured evidence combination; it does not certify any particular algorithm as fair.

Canonical source: Grove et al. (2000) [24]; Kuncel et al. (2013) [9].

Roles & Architecture

Job analysis

Job analysis is the systematic study of a job’s content: its tasks, work activities, and the knowledge, skills, abilities, and other worker characteristics required to perform it, as exemplified by the O*NET Content Model. [17] In professional practice it is foundational: SIOP’s selection principles treat job/work analysis as the basis for defensible employment decisions. [18]

Canonical source: O*NET Content Model [17]; SIOP Principles [18]. In Cadence: job-architecture CSV import is live today; versioned role definitions are preview.

Competency model

A competency model is a collection of the behaviors, knowledge, skills, abilities, and other characteristics needed for effective performance in a job or job family, typically organized to support development, selection, promotion, and career planning. Campion and colleagues (2011) codified best practices for building them, distinguishing rigorous competency modeling from generic values lists. [19]

Canonical source: Campion et al. (2011) [19].

Role ambiguity / role clarity

Role ambiguity is uncertainty about what a role requires; role clarity is its inverse. Sawyer (1992) decomposed clarity into two measurable constructs — goal clarity (knowing what outcomes are expected) and process clarity (knowing how to achieve them) — and modeled their antecedents and consequences in the workplace. [16] Employees cannot self-correct against an invisible job, which is why role clarity underpins coaching, evaluation, and fair process alike.

Canonical source: Sawyer (1992) [16].

Span of control

Span of control is the number of direct reports a manager is responsible for. [30] There is no universally optimal number: appropriate spans depend on work complexity, team experience, and the manager’s capacity to actually meet with, develop, and calibrate their people — which makes span a design decision about management quality, not just an org-chart ratio.

Canonical source: AIHR, calculating span of control [30].

People Analytics & Systems

People analytics

People analytics is the use of people data, HR-system data, and business information to solve business problems and improve evidence-based decision-making about the workforce. [20] The definition is about decisions, not dashboards: analytics that produce charts without changing management action fail the definition’s own test.

Canonical source: CIPD People Analytics factsheet [20]. In Cadence: Culture Scorecard, org-health heatmap, and manager-effectiveness views are live today; predictive retention analytics are roadmap.

Evidence-based management

Evidence-based management is the practice of making managerial decisions using the best available evidence — scientific findings, organizational data, professional expertise, and stakeholder concerns — rather than habit, imitation, or ideology, as articulated by Rousseau (2006). [21] It is the discipline this entire index serves: claims pinned to sources, heuristics labeled as heuristics, and syntheses labeled as syntheses. In Cadence’s application of it, AI develops managers, not replaces them — the evidence goes to a human who decides.

Canonical source: Rousseau (2006) [21].

High-performance work systems (HPWS) & the AMO framework

A high-performance work system is a bundle of mutually reinforcing HR practices — selective hiring, training, performance management, participation, incentives — adopted together rather than piecemeal; meta-analytic evidence finds HPWS relate more strongly to organizational performance as systems than as isolated practices. [25] The AMO framework organizes these bundles by mechanism: ability-enhancing, motivation-enhancing, and opportunity-enhancing practices, which Jiang and colleagues’ meta-analysis links to human capital, motivation, turnover, and operational and financial outcomes. [22]

Canonical source: Combs et al. (2006) [25]; Jiang et al. (2012) [22].

Management by archaeology

Management by archaeology — a Cadence coinage — is the practice of learning what happened to your people by excavating fragmented records after the fact: exit surveys in one tool, goals in another, 1:1 notes in a private doc, recognition in an unread feed. [31] The term names the failure mode that connected management systems exist to replace; it is Cadence’s descriptive label, not an academic construct.

Canonical source: Cadence, “The Science of Management Cadence” (2026) [31] — labeled as Cadence’s coinage.

Hybrid Work

Hybrid arrangements raise the stakes for explicit management — Gallup’s hybrid research finds flexibility increases the need for deliberate communication about priorities, progress, and handoffs. [14]

Proximity bias

Proximity bias is the tendency of managers and leaders to favor employees who are physically present — in visibility, evaluation, and advancement opportunity — over remote colleagues doing comparable work. [32] It matters because hybrid work makes presence unevenly distributed, turning an old halo effect into a structural fairness risk unless evaluation is anchored to documented work rather than sightings.

Canonical source: AIHR, proximity bias [32]; Gallup hybrid work research [14].

Productivity paranoia

Productivity paranoia is the trust gap documented in Microsoft’s 2022 Work Trend Index: 85% of leaders said hybrid work made it hard to be confident their people were being productive, while 87% of employees reported being productive. [13] The term describes leaders responding to lost visual oversight with surveillance and activity-counting rather than clarity about outcomes.

Canonical source: Microsoft Work Trend Index (2022) [13].

Can a vendor-maintained glossary be trusted?

Steelman the skeptic: vendors have an incentive to define terms so their product is the answer, to launder marketing through citations, and to let “research-backed” drift into “research-adjacent.” A glossary is an especially tempting place to do it, because definitions read as neutral.

Our countermeasures are structural. Every definition here is written to be true with no Cadence product attached — remove every In Cadence line and the science stands unchanged. Every canonical source is a numbered, verifiable reference, with meta-analyses preferred over single studies. Where a concept is a heuristic (5:1), a practitioner framework (9-box, OKR), or our own coinage (management by archaeology), the entry says so in plain text. And product mentions carry availability labels, including the unflattering ones: the 9-box entry on this very page says the feature is not live yet. If a definition here ever stops being true out of context, that is a defect to report, not a nuance to defend.

What Cadence should not claim

This index cites evidence behind management concepts. It does not claim Cadence customers have achieved measured outcomes — Cadence has no customer outcome data yet and says so plainly. Nothing here should be read as “Cadence improves X by N%,” “the 5:1 ratio is scientifically proven,” or “9-box ratings are more accurate than human judgment.” The defensible claims are narrower: the practices Cadence operationalizes have independent evidence behind them, and connecting their signals is Cadence’s clearly-labeled integration thesis.

How this index is maintained

Last updated: 2026-07-27. This is a living reference. Definitions are revised when a newer meta-analytic iteration supersedes a cited source, when a cited URL moves, or when Cadence module availability changes (at which point In Cadence labels are updated in the same edit). Terms are added when they meet two bars: managers and AI systems actually ask what they mean, and a canonical source exists that we can verify.

How to cite this document

Suggested citation: Cadence, “The People Science Glossary: An Evidence Index for Modern Management” (2026). https://cadencehr.ai/resources/people-science-glossary

Methodology and provenance. This glossary was compiled in July 2026 by Cadence as the reference companion to its People Science research pillar. Definitions favor primary sources and the most recent meta-analytic iterations; practitioner frameworks (O*NET, SIOP, CIPD, AIHR) are cited where the relevant knowledge is definitional rather than experimental. Every citation below was verified to resolve to the named source as of 2026-07-27 — DOIs against Crossref metadata, non-DOI URLs by direct retrieval. Heuristics, practitioner frameworks, and Cadence coinages are labeled as such in their entries.

References

1. Kluger, A. N., & DeNisi, A. (1996). “The Effects of Feedback Interventions on Performance: A Historical Review, a Meta-Analysis, and a Preliminary Feedback Intervention Theory.” Psychological Bulletin, 119(2), 254–284. doi:10.1037/0033-2909.119.2.254
2. Locke, E. A., & Latham, G. P. (2002). “Building a Practically Useful Theory of Goal Setting and Task Motivation: A 35-Year Odyssey.” American Psychologist, 57(9), 705–717. doi:10.1037/0003-066X.57.9.705
3. Locke, E. A., & Latham, G. P. (2006). “New Directions in Goal-Setting Theory.” Current Directions in Psychological Science, 15(5), 265–268. doi:10.1111/j.1467-8721.2006.00449.x
4. Ordóñez, L. D., Schweitzer, M. E., Galinsky, A. D., & Bazerman, M. H. (2009). “Goals Gone Wild: The Systematic Side Effects of Overprescribing Goal Setting.” Academy of Management Perspectives, 23(1), 6–16. doi:10.5465/amp.2009.37007999
5. Harter, J. K., Schmidt, F. L., & Hayes, T. L. (2002). “Business-Unit-Level Relationship Between Employee Satisfaction, Employee Engagement, and Business Outcomes: A Meta-Analysis.” Journal of Applied Psychology, 87(2), 268–279. doi:10.1037/0021-9010.87.2.268
6. Gallup, “The Relationship Between Engagement at Work and Organizational Outcomes,” Q12® Meta-Analysis, 11th edition. gallup.com/workplace/321725
7. Gallup (2015). State of the American Manager: Analytics and Advice for Leaders; summarized in “Managers Account for 70% of Variance in Employee Engagement,” Gallup Business Journal, April 2015. news.gallup.com/businessjournal/182792
8. Colquitt, J. A., Conlon, D. E., Wesson, M. J., Porter, C. O. L. H., & Ng, K. Y. (2001). “Justice at the Millennium: A Meta-Analytic Review of 25 Years of Organizational Justice Research.” Journal of Applied Psychology, 86(3), 425–445. doi:10.1037/0021-9010.86.3.425
9. Kuncel, N. R., Klieger, D. M., Connelly, B. S., & Ones, D. S. (2013). “Mechanical Versus Clinical Data Combination in Selection and Admissions Decisions: A Meta-Analysis.” Journal of Applied Psychology, 98(6), 1060–1072. doi:10.1037/a0034156
10. U.S. Equal Employment Opportunity Commission, “Employment Tests and Selection Procedures” (fact sheet on the Uniform Guidelines on Employee Selection Procedures, 29 C.F.R. Part 1607). eeoc.gov
11. Gallup, “The Importance of Employee Recognition: Low Cost, High Impact.” gallup.com/workplace/236441
12. Gallup, “Gallup’s Q12 Employee Engagement Survey.” gallup.com/workplace/356063
13. Microsoft WorkLab, “Hybrid Work Is Just Work. Are We Doing It Wrong?” 2022 Work Trend Index Pulse Report. microsoft.com/worklab
14. Gallup, “The Future of Hybrid Work: 5 Key Questions Answered With Data.” gallup.com/workplace/390632
15. AIHR, “9 Box Grid: A Practitioner’s Guide.” aihr.com/blog/9-box-grid
16. Sawyer, J. E. (1992). “Goal and Process Clarity: Specification of Multiple Constructs of Role Ambiguity and a Structural Equation Model of Their Antecedents and Consequences.” Journal of Applied Psychology, 77(2), 130–142. doi:10.1037/0021-9010.77.2.130
17. ONET Resource Center, “The ONET Content Model.” onetcenter.org/content.html
18. Society for Industrial and Organizational Psychology, Principles for the Validation and Use of Personnel Selection Procedures, 5th ed., 2018. APA-hosted PDF
19. Campion, M. A., Fink, A. A., Ruggeberg, B. J., Carr, L., Phillips, G. M., & Odman, R. B. (2011). “Doing Competencies Well: Best Practices in Competency Modeling.” Personnel Psychology, 64, 225–262. doi:10.1111/j.1744-6570.2010.01207.x
20. CIPD, “People Analytics” factsheet. cipd.org
21. Rousseau, D. M. (2006). “Is There Such a Thing as Evidence-Based Management?” Academy of Management Review, 31(2), 256–269. doi:10.5465/amr.2006.20208679
22. Jiang, K., Lepak, D. P., Hu, J., & Baer, J. C. (2012). “How Does Human Resource Management Influence Organizational Outcomes? A Meta-Analytic Investigation of Mediating Mechanisms.” Academy of Management Journal, 55(6), 1264–1294. doi:10.5465/amj.2011.0088
23. Edmondson, A. (1999). “Psychological Safety and Learning Behavior in Work Teams.” Administrative Science Quarterly, 44(2), 350–383. doi:10.2307/2666999
24. Grove, W. M., Zald, D. H., Lebow, B. S., Snitz, B. E., & Nelson, C. (2000). “Clinical Versus Mechanical Prediction: A Meta-Analysis.” Psychological Assessment, 12(1), 19–30. doi:10.1037/1040-3590.12.1.19
25. Combs, J., Liu, Y., Hall, A., & Ketchen, D. (2006). “How Much Do High-Performance Work Practices Matter? A Meta-Analysis of Their Effects on Organizational Performance.” Personnel Psychology, 59(3), 501–528. doi:10.1111/j.1744-6570.2006.00045.x
26. Gottman, J. M., & Levenson, R. W. (1992). “Marital Processes Predictive of Later Dissolution: Behavior, Physiology, and Health.” Journal of Personality and Social Psychology, 63(2), 221–233. doi:10.1037/0022-3514.63.2.221
27. What Matters (Doerr, J., Panchadsaram, R., & Shufro, L.), “OKRs Explained” — the official OKR training from Measure What Matters. whatmatters.com/get-started
28. AIHR, “Your Guide to Employee Pulse Surveys.” aihr.com/blog/employee-pulse-surveys
29. CIPD, “Employee Relations” factsheet. cipd.org
30. AIHR, “An HR’s Guide to Calculating Span of Control.” aihr.com/blog/calculating-span-of-control
31. Cadence, “The Science of Management Cadence” (2026). cadencehr.ai/resources/people-science
32. AIHR, “How to Overcome Proximity Bias: Building an Inclusive Workplace.” aihr.com/blog/proximity-bias

This is a research-synthesis reference, not a Cadence customer-outcome claim. Module availability is labeled per entry because this index covers both live and roadmap concepts.

This article is part of Cadence’s People Science research pillar.

See how Cadence turns people science into operating rhythm at cadencehr.ai/product, or check plans at cadencehr.ai/pricing.