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Record W3193570394 · doi:10.1177/23792981211037255

Who Gets Time Off? Prioritizing Planned, Family Responsibility Leave Requests

2021· article· en· W3193570394 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueManagement Teaching Review · 2021
Typearticle
Languageen
FieldPsychology
TopicTeam Dynamics and Performance
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsDebriefingPsychologyHuman resource managementTeam compositionKnowledge managementApplied psychologySocial psychologyComputer science

Abstract

fetched live from OpenAlex

This role play focuses on team decision making and is designed for undergraduate and graduate human resource management (HRM) and organizational behavior (OB) courses. It can also support management seminars. Working within Employee Teams, Department Teams, or Manager Teams, students decide which three of five employees will obtain family responsibility leave. For HRM courses, the exercise focuses on interpreting and applying family responsibility leave, which illustrates day-to-day personnel planning. For OB courses, the debriefing centers on comparing decision-making models and discussing how beliefs and attitudes influence decision making; it also supports exchanges about the influence of conflict, domination, and groupthink on team decision making. For both OB and HRM courses, the exercise helps students compare individual and team decisions, discuss the effects of team composition on decision making, and analyze the fairness of their decisions. Instructors can conduct the activity in class or online.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.892
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.022
GPT teacher head0.333
Teacher spread0.311 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it