Expert Memories: The Professional Construction of the Past and the Mnemonic Making of Occupations
Bibliographic record
Abstract
Abstract This article introduces the special issue on occupations and memory in organizations. To foster increasing collaboration from scholars from both fields, we offer a general argument connecting memory and occupations on two levels. At the societal level, we show how memory experts, such as historians, archivists, and museologists, have played a fundamental role in the development of modernity and the emergence of our contemporary historical consciousness. At the occupational level, we argue that occupations are transgenerational communities maintained through various practices and technologies of memory whose legitimacy and professional status increasingly depend on their ability to cultivate both practical and historical memory. We further explore three related topics covered by the papers from this special issue: expert and memory work, occupational and mnemonic communities, and professional and mnemonic projects. At the end, we identify three promising themes for future research: the negotiation of boundaries and resources among communities; the interaction between technology, expertise, and memory; and the occupational ethics and responsibility towards past actions and memories.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 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".