Den profesjonelle førskolelærer: profesjonsoppgave pedagogikk
Bibliographic record
Abstract
Hvordan forstår førskolelæreren begrepet danning, og hvordan kan førskolelæreren fremme\nbarnas danningsprosesser gjennom lek i barnehagen?\nJeg ønsker å se nærmere på leken som danningsprosess. Det gjør det lettere å holde rammene\nog få et avgrenset fokus fordi danningsbegrepet rommer så mye. Jeg har valgt å se på hva\nulike fagpedagoger sier om danningsbegrepet for å få frem mangfoldet i definisjonene. Jeg\npresenterer derfor ikke en definisjon av begrepet. Jeg har på tampen valgt å bytte ut ordet\nivareta i min opprinnelige problemstilling til ordet fremme (se intervjuguide hvor\nopprinnelige problemstilling er presentert). Grunnen er at barnehagen skal fremme danning.\nSlik jeg ser det, stiller det større krav til førskolelæreren om en mer aktiv rolle i møte med\nbarnets danningsprosesser.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.034 | 0.012 |
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".