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Performance Management

2009· book-chapter· en· W4254895964 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

VenueOxford University Press eBooks · 2009
Typebook-chapter
Languageen
FieldPsychology
TopicHuman Resource Development and Performance Evaluation
Canadian institutionsWilfrid Laurier UniversityUniversity of WaterlooUniversity of Toronto
Fundersnot available
KeywordsCoachingPerformance appraisalSet (abstract data type)Process (computing)Process managementPerformance managementEmployee Performance AppraisalPsychologyComputer scienceOperations managementKnowledge managementEngineeringBusinessManagementMarketingMedicine

Abstract

fetched live from OpenAlex

Abstract A distinguishing feature of performance management relative to performance appraisal is that the former is an ongoing process whereas the latter is done at discrete time intervals (e.g. annually). Ongoing coaching is an integral aspect of performance management. Performance appraisal is the time period in which to summarize the overall progress that an individual or team has made as a result of being coached, and to agree on the new goals that should be set. Common to the performance management/appraisal process are the four following steps. First, desired job performance must be defined. Second, an individual's performance on the job must be observed. Is the person or team's performance excellent, superior, satisfactory, or unacceptable? Third, feedback is provided and specific challenging goals are set as to what the person or team should start doing, stop doing, or be doing differently. Fourth, a decision is made regarding retaining, rewarding, training, transferring, promoting, demoting, or terminating the employmemt of an individual.

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)0.000
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 score0.981
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.039
GPT teacher head0.245
Teacher spread0.205 · 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