People who teach regularly: What do we know from PIAAC about their professionalization?
Bibliographic record
Abstract
Professionalization in adult education is necessary, and several initiatives are underway to improve the professional situation as well as the competences and skills of adult educators. The relevance and importance of adult education is often stated. Large-scale assessments such as the Programme for the International Assessment of Adult Competences show how important adult education is for societies and economies. They give information on participation and participants. At first glance, the Programme for the International Assessment of Adult Competences lacks detailed information on adult education institutions and professions. A second glance allows explore what people do as part of their work. Those who state that they teach regularly or occasionally will be explored here in more detail. Findings from this study reveal several characteristics of people who teach, including age, gender, academic background and industries. In particular, our analysis suggests that more than 80% of those who teach did not have formal degrees in education sciences. Moreover, those who teach frequently have higher skills, older ages and they have better job positions than those who do not teach. The majority of those who teach are males. Lastly, the results indicate that seniority and prestige in all 14 countries examined in this study are highly relevant to people who teach.
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".