Terms of use and network size: Evidence from online job boards and CV banks in the U.S.
Bibliographic record
Abstract
Data on websites that hosted job boards and CV banks in the U.S. from 2000 to 2011 reveal that websites imposed fewer restrictions (in terms of the duration of use) and lower fees for job searchers relative to employers. This asymmetry in the treatment (or the terms of use) changed as the relative scarcity of job searchers and job vacancies in the labor market in which the websites offered their services changed. Compared with job searchers, employers faced less stringent restrictions and lower fees when job searchers were scarce relative to job openings. These adjustments imply that the value of using an employment website changes with the number of potential users and the probability of finding a quality match. We find that these adjustments were most pronounced for websites that relied exclusively on employers and job searchers for their content (job ads and CVs). Whereas existing literature on the role that network size plays in intermediaries’ decision-making has focused on prices, our findings reveal that this focus can overlook other adjustments that affect the terms of use. Given that these adjustments in our context may result in longer periods during which CVs and job ads remain online, our findings suggest that the optimal design of intermediaries must include tools that help users sort through stale information.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".