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Record W3002516985 · doi:10.1177/1477971419898493

People who teach regularly: What do we know from PIAAC about their professionalization?

2020· article· en· W3002516985 on OpenAlexaff
Anke Grotlüschen, Christopher Stammer, Thomas J. Sork

Bibliographic record

VenueJournal of Adult and Continuing Education · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Educational Policies and Reforms
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsProfessionalizationAdult educationSeniorityLifelong learningPrestigeRelevance (law)Higher educationPedagogyInstitutionalisationScale (ratio)Medical educationPsychologyWork (physics)SociologyPolitical scienceSocial scienceMedicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.498
Threshold uncertainty score0.328

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.288
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2020
Admission routes1
Has abstractyes

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