Employment Predictions in Secretarial Occupation
Bibliographic record
Abstract
The developments in technology and the intense business life makes secretarial work as an indispensable component of business administration. Besides on the job training and inservice training at workplaces,with the education given at Secretarial Vocational Lycees, at Vocational High Schools and at university levels, quailfied employees are trained. Also the spread of the occupational secretarial courses (on topics such as; touch-type, computer, diction, pre-accountancy) increases the number of qualified personnel. Although the increase in the number of vocational high schools gives rise to the higher amount of qualified secretaries, the results of the research reveals that the numer of secretaries without vocational education in the occupation is more than the qualified ones in the same field. On the other hand, it is observed that educated secretaries might become unemployed as well.In this study, determining the employment gap in secreterial vocation by examining the general censuses was aimed.In the study General Population Censuses which takes place one in five years (1970, 1975, 1980, 1985, 1990, 2000) were examined. In these populatiun censuses, participants were surveyed about their main occupation and asked what their job was within the last week and the answers given by the citizens were taken into account in this study. Questions that were answered open-ended were classified according to ISCO 88 International Standard Classification of Occupations. The number of graduates of lycees and high schools that are providing secreterial education were taken from The Ministry of Education and Higher Education Board. It was determined that employment gap in secreterial occupation might be closed by the year 2025 according to the projections made.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".