Organizational Career Management: Researching at Managerial Philosophy level
Bibliographic record
Abstract
The research on organizational career management is much less than career self-management. There are several taxonomies on dividing organizational career management, but lack at Philosophy level. At managerial Philosophy level, the enterprise can accept not only collectivism or individualism but also equilibriumism value. With collectivism or individualism value, what the enterprise develops is non-systematic organizational career management. While with equilibrium value, the enterprise may develop systematic career management. Key words: Equilibriumism, Career development, Organizational career management Resume: Les recherches sur la gestion organisationnelle de carriere sont moins nombreuses que celles sur l’auto-gestion de carriere. Il y a plusieurs categories de gestion organisationnelle de carriere, mais au niveau philosophique il n’y en a pas. Au niveau philosophique de la gestion, l’entreprise peut accepter non seulement le collectivisme ou l’individualisme, mais aussi la conception de valeur d’equilibrisme. Avec le collectivisme ou l’individualisme, ce que l’entreprise developpe est la gestion organisationnelle de carriere non-systematique. Alors avec l’equilibrisme, l’entreprise peut developper la gestion systematique de carriere. Mots-Cles: equilibrisme, developpement de la carriere , gestion organisationnelle de carriere, gestion systematique de carriere, gestion organisationnelle de carriere non-systematique
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".