Ethnographic sensitivity and current recordkeeping
Bibliographic record
Abstract
Purpose The purpose of this paper is to report on the application of information culture analysis techniques in the workplace. The paper suggests that records managers should use ethnographic sensitivity, if they want to have a constructive dialogue with records creators and users, and effect positive change in their organisations. Design/methodology/approach Two pilot studies were conducted in university settings for the purpose of testing an information culture assessment toolkit. The university records managers who carried out the investigation approached the fieldwork ethnographically, in the sense that they were interested in the perspectives of their end users, and tried to understand their information cultures, rather than imposing their recordkeeping concepts and procedures. Findings Information culture analysis was of practical utility in large complex organisations, providing an insight into behaviours, motivations, and most importantly promoted reflection and dialogue among organisational actors. Originality/value The paper raises awareness of the diversity of professional skills and knowledge required by records practitioners. It emphasises that to remain relevant to their organisations, records managers have to be receptive and sensitive to cultural influences.
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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 | 0.056 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.007 | 0.029 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".