Defiant Objects: Non-standard research outputs in Institutional Repositories
Bibliographic record
Abstract
The Defiant Objects project was an 18-month Sherpa-LEAP research project, looking at depositing non-standard or difficult research outputs (which we have termed 'defiant objects') into institutional repositories. Headed by Tahani Nadim (Goldsmiths) with research assistance from Rebecca Randall (Goldsmiths). It followed on from work carried out by the LEAP Media Working Group into depositing multi media work into repositories. Defiant Objects addresses a number of issues that have arisen in conjunction with the increase in non-text-based, non-standard or otherwise difficult deposits in repositories: deciding what exactly to upload, the relationship between a work and it's surrogate, choice of appropriate item type, metadata or controlled terminology, and completing item records in such a way as to be clear and comprehensible to end users of the repository. Outputs from this project include a deposit guide poster and leaflet, a project report, a blog and twitter feed and will also include a conference poster for Open Repositories 2013 in Canada.
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 | 0.089 | 0.155 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.022 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.031 | 0.036 |
| Open science | 0.006 | 0.028 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.040 | 0.018 |
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".