Fostering a career development culture: reflections on the roles of managers, employees and supervisors
Bibliographic record
Abstract
The culture of an organization can be positive and supportive, or threatening and destructive. A career development culture helps address productivity, competitiveness, affirmative action, and succession planning. It helps people redefine their talents to realize the full potential of their jobs. Supervisors should play a key role in creating a career development culture, but many feel their careers are going nowhere and see career development efforts to be an added burden. Supervisors seldom do performance appraisals properly because they are afraid of their workers and the workers are virtually paranoid about the slightest negative note on their files. A better way is to organize a system of mentorship. Evaluation of initiatives can be calculated on the basis of savings that can be attributed to the program and its actual costs. A managed career development culture can pay great rewards to an organization and the people working in it.
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.
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 | 0.022 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.014 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.002 | 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".