Moving Technical Reports Forward
Bibliographic record
Abstract
Technical reports have always posed problems for libraries and librarians.They are often bibliographically inconsistent, difficult to source, and published to varying standards of quality.In some science and technical fields, these reports are also large in number and central in importance.Additionally, established workflows for acquiring and preserving technical reports in distributed repositories have been undermined by the transition from print to digital.Overall, the "grey literature" challenges librarians face have increased.This paper presents three case studies of how academic libraries have found innovative ways to face the problems of technical reports and improve their production, dissemination, and preservation; thus reducing the duplication of research efforts and saving public funds.Transportation is one example of the disciplines where these described changes are taking place, and the opportunities for libraries to improve the technical report workflow in this field will be a particular focus of the session.Readers can expect to learn about the challenges of handling technical reports in the digital age and the opportunities that exist for improving discoverability and dissemination in the networked environment.A particular focus will be on new roles for libraries and librarians, and how library publishing and data management services can offer new opportunities for partnerships with researchers.
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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 | 0.017 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.036 | 0.039 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.087 | 0.104 |
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".