Archivist on Board: Contributions to the Research Team
Bibliographic record
Abstract
In this article, we demonstrate the advantages of including an archivist as a member of the research team based on our experience with two multi-site, multi-method, primarily qualitative projects. The Principal Investigator, committed to the principles of data sharing and preservation, recruited a data archivist at the inception of the projects. Several issues arose that are not typically encountered in a research project: investigators needed to agree to the principles of data preservation and sharingconcepts that are not typically discussed a prior; the research ethics application and approval had to incorporate the conditions of preservation and sharing; and we needed a comprehensive plan for preservation that would ensure the creation of high-quality data products worthy of deposition. This comprehensive plan required that we identify the standards of archiving, incorporating within the data management plan an appropriate inventory list and a design for tagged fields and a corresponding Document Type Definition (DTD) used in the mark-up of textual data. A plan for creating access to the data for secondary analysis was also developed. The conditions of use, cataloguing records, and citation guide are all part of preparing the data for access. Finally, the challenges of this approach are summarized. URN: urn:nbn:de:0114-fqs000356
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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 | 0.133 | 0.204 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.028 | 0.018 |
| Scholarly communication | 0.027 | 0.016 |
| Open science | 0.005 | 0.033 |
| Research integrity | 0.006 | 0.022 |
| Insufficient payload (model declined to judge) | 0.024 | 0.008 |
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".