11:15am Building/Room: Marriott Montreal Chateau Champlain / Salle de Bal Ballroom &
Bibliographic record
Abstract
This contribution follows the order of the Abstract and Summary in the AERA 2005 on-line Conference Programme and will focus on educational influences in my learning from the 31 year research programme into a reconstruction of educational theory described in the Abstract. The contribution is focused on relating my learning, from my s-step research, to answering the questions and justifying the claims made in the Summary below. The questions to be answered and the claims to be justified are underlined in the Summary for emphasis. The answers and justifications are included in the Summary in bold italics with an additional reference section of author and dates to distinguish them from the questions, claims and references made in the original proposal with a former colleage, Sarah Fletcher. The placing of this contribution by the SIG programme chair into a session on Renewing the Craft of Self-Study is particularly appropriate to researching a process of enhancing educational influence that involves a commitment to living values of humanity more fully in practice. The paper is focused on research into issues of demography and democracy explicated by our web-based resources that influence our values-based standards of accountability we aspire to
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.826 | 0.477 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".