Towards minimum quality criteria for mentoring-to-work programmes
Bibliographic record
Abstract
An instrument that is increasingly being used to integrate people of foreign origin into the labor market is "mentoring-to-work" in which a newcomer (mentee) and a volunteer with knowledge of the local labor market (mentor) are paired ("matched") so that the latter can help the mentee on the road to employment. It is a relatively new concept that is gaining popularity in Europe and Canada. An evaluation of start-up projects showed that mentoring practices were not always optimal. (Vandermeerschen & De Cuyper: 2018). In order to ensure that mentors and mentees can benefit from quality mentoring, regardless of the specific approach of the project, we developed minimum quality criteria for mentoring-to-work projects. Based on a comparison of 7 existing quality labels in the broader field of mentoring, a set of criteria was selected applicable to mentoring-to-work. These criteria were tested through a series of workshops with mentoring organizations in order to determine (1) their applicability to the field of 'mentoring-to-work' (2) which criteria were missing (3) whether consensus could be reached on minimum criteria. The strength of this contribution lies in the fact that it is grounded in the reality of 'mentoring to work', but also builds on both scientific and practice-related expertise in other fields of mentoring. In this respect, the result of this paper is not only a set of minimum quality criteria applicable to the field of mentoring-to-work, but also a systematic overview of existing quality criteria and a starting point and method to develop criteria within other (national) contexts and domains of mentoring.
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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 | 0.112 | 0.283 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".