Embedding a records manager as a strategy for helping to positively influence an organization’s records management culture
Bibliographic record
Abstract
Purpose This paper aims to explore the benefits of embedding a records manager into a team of university administrators to help them address their information management needs. Design/methodology/approach The paper describes an experience that was inspired by reports of successful experiences with embedded librarianship. The literature on records management culture and embedded librarianship is reviewed to identify best practices and criteria for success. These criteria are used to design and implement a pilot project where, rather than hiring a consultant, a records manager is embedded into a quality assurance team working at a large university in Canada. Findings The project is a success in conventional terms (e.g. active files reduced; duplicates deleted; inactive files archived; naming conventions, version control and access rights applied); however, similar results could have been achieved using a consultant. More interesting are the added benefits achieved through embedding. Added benefits included identifying workflow inefficiencies, identifying terminological inconsistencies, iterative training opportunities and useful knowledge sharing outside the project’s scope. The argument is made that an embedded information professional is better able to appreciate the organizational culture, which in turn facilitates the establishment of trusted relationships and produces an overall added value for the entire team. Originality/value There is very little, if any, current literature that explores the value of embedding a records manager into a team, rather than simply hiring a consultant to address information management needs. The outcome of this pilot project will benefit those who are seeking to develop a model for embedding an information professional into their organization to gain an added value.
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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 | 0.037 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".