Identifying unreliable sources of skill and competency information
Bibliographic record
Abstract
Organizations need to accurately understand the skills and competencies of their human resources in order to effectively respond to internal and external demands for expertise and make informed hiring decisions. In recent years, however, human resources have become highly mobile, making it more difficult for organizations to accurately learn their competencies. In such environment, organizations need to rely significantly on third parties to provide them with useful information about individuals. These sources and the information they provide, however, vary in degrees of trust and validity. In a previous paper, we developed an ontology for skills and competencies and modeled and analyzed the various sources of information used to derive the belief in an individual's level of competency. In this paper, we present an approach based on social network analysis for identifying unreliable sources of competency information. We explore the conditions under which evaluations given by an individual or a group about another can be trusted. We evaluate this approach using recommendation data gathered by crawling user profiles in LinkedIn.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".