Mapping the Fit Between Research and Multimedia: A podcast exploration of the place of multimedia within / as scholarship
Bibliographic record
Abstract
Using the medium of the podcast this piece highlights key factors that may have an impact on how multimedia is used by educational researchers. The author examines the degree of congruence between the prevailing norms of representation in educational research and the norms and processes of multimedia as a way of presenting knowledge and information. The podcast also explores the extent to which multimedia is a usable resource in schools, and whether the skill sets and inquiry processes of educational researchers are compatible with the rip / burn / remix manifestos of multimedia and maker cultures. The author / podcaster concludes that changes in the adoption and use of multimedia within / as scholarship will evolve over time as more and more individuals learn how to produce multimedia content, while at the same time, consumers of educational research are becoming acclimatized to increased diversity in forms of knowledge representation.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.009 | 0.019 |
| Scholarly communication | 0.022 | 0.014 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".