Sélection des méthodes de cueillette de l’information dans la recherche d’emploi : une analyse empirique
Bibliographic record
Abstract
Assuming that differences in the cost of using various job search methods are negligible, the authors suggest that the job searcher makes use of specific combinations of complementary methods, and that the composition of these combinations reflects relevant personal characteristics of the job searcher (the structure of labour supply) and the searcher's perception of the characteristics of vacancies (the structure of labor demand). This hypothesis is analysed with the help of data collected by a questionnaire mailed to 1100 blue- and white-collar workers laid off from a Montreal aeronautical firm from January 1970 to March 1971. Components analysis is used to identify four combinations of job-search methods: 1) ex-employer, trade union or employees' association, special placement service (FD1); 2) private agencies, newspaper advertisements, manpower centres (FD2); 3) parents or friends, manpower centres, newspaper advertisements (FD3); personal initiatives (FD4). An analysis of these results suggests two new concepts: standard methods, i.e. those widely and habitually used by job searchers in general; supplementary methods, i.e. those used when standard methods fail to provide what the job searcher considers a satisfactory chance of getting a job. Combination FDI is interpreted as one of supplementary methods. The second part of the article relates 64 variables, reduced to 21 factors representing various characteristics of job searchers (personal, occupational, financial, labour market behavior), to the four combinations of search methods, in an attempt to explain part of the method selection process. It is found that users of FD2 are relatively qualified, English-speaking, mobile and interested in training, and are likely to have found a job. Users of FD3 are characterized by their youth and their flexibility in terms of jobs and mobility. FD1, by contrast, appears to have been used by older workers with family responsibilities, who are likely to have remained continually unemployed after lay-off. This is seen as supporting the interpretation of FD1 as a combination of supplementary methods. Finally, the results indicate that the difference in behaviour between white- and blue-collar workers may not be as clear-out as is suggested in the literature; the demarcation appears to rest on the level of skill in either group, rather than on the collar-status itself. It is concluded that the data analysed lend some support to the hypothesis formulated. Combinations of search methods detected by components analysis do appear to be related to identifiable categories of job searchers. The skill level is seen as a particularly important determinant of the selection process. Moreover, it is suggested that the concept of supplementary methods is sufficiently sustained by the data to warrant further study.
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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 | 0.231 | 0.475 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.010 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".