Opportunités et contraintes dans l’exercice de la fonction « formateur interne » dans les entreprises publiques de côte d’ivoire. Cas de la Direction Générale des Impôts
Bibliographic record
Abstract
Cette etude vise a connaitre les difficultes rencontrees dans l’exercice de la fonction ‘‘formateur interne’’ et les opportunites offertes aux formateurs internes de la DGI. Les recherches ont eu lieu a la Direction Generale des Impots (DGI), sise au Plateau et se sont appuyees sur 58 participants issus de l’ensemble des salaries formateurs et stagiaires de ladite structure. Les donnees ont ete recueillies a l’aide d’un questionnaire d’enquete, puis analysees d’un point de vue quantitatif et qualitatif. Les resultats obtenus indiquent qu’en depit des nombreuses opportunites que regorge la fonction « formateur interne » (l’acquisition d’habiletes professionnelles, le developpement de competences transversales telles que des competences d’ordre social, personnel et intellectuel, etc.), celle-ci fait face a de nombreuses difficultes. Il s’agit de la gestion du temps pour realiser a la fois les tâches administratives et pedagogiques, du sentiment de manque de soutien de la part de la hierarchie, de l’insuffisance des moyens d’incitation pour les motiver, de volumes horaires insuffisant pour certains enseignements, de difficultes dans le choix d’une strategie pedagogique pertinente, lors de la conception des modules de cours et de l’utilisation des supports pedagogiques, etc.
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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 | 0.019 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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.
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".