Bibliographic record
Abstract
Nowadays, coming to the middle of the first quarter of the21th century, the flow of life is faster than ever; social, culturateconomic and technological changes affect the approachesand the canception of the education. Frequentlyrevised education programmes introduce also a need of developingnew teaching methods and materials appropriateto themselves. Meeting the qualifications of this need "photointerpretation", as a method of values education, is one of the appropriate methods of today's constructivist educationand student-centered teaching progr amme.Photo interpretation is a measuring and evaluatingmethod both theoretically and practically appropriate to thegoals of values education.In this article it is discussed what the photo interpretationis, its advantageous and limited aspects and how to apply.Moreover, giving two sample applications, the phasesand characteristics are indicated.These two sample applications were carried out by theteachers in various branches in four different educational institutions.Before the sample application, method related informationand materials were given to the teachers. At theend of the sample applications, face to face interviews weremade with the applying teachers; then, the results and reviewswere produced.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.044 | 0.007 |
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".