Réactions du chef de PME après utilisation d'un système d'évaluation multi-source
Bibliographic record
Abstract
In a context of intense economic competition, organizations are increasingly using instruments of performance evaluation.The multi-source feedback or 360 0, is one of those.The literature seems still silent on what type of evaluation is really about the reaction it generates among evaluated.In response to a request from the Groupement des chefs d'entreprise du Qubec (GCEQ), a system of multi-source assessment was designed by the Laboratoire de recherche sur la performance des entreprises (LaRePe).The PDG-Leadership, specifically used to measure the skills of managers of SMEs as a leader.After sorne years of use, developers want to better understand its impact in order to improve it and make it ev en better.To address these theoretical and practical considerations, a survey was conducted among 87 business leaders from Quebec who had already been assessed using this tool.This research has the purpose, the validation of a preliminary model proposed by Smither, London, and Reilly, 2005a, to examine the variables that influence that evaluated undertake actions as a result of their feedback and the other, to know these actions, in short, that the system of feed-back multi-source (FMS) really.From the analysis of data collected, a list of 112 shares was established.ln turn, this led to a categorization of actions taken.Although the FMS system is effective, it should be noted that entrepreneurs seem to react differently from other categories assessed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".