History as Organizing: Uses of the Past in Organization Studies
Bibliographic record
Abstract
Research on the “uses of the past” in organizations and organizing is flourishing. This introduction reviews this approach to integrating history into organization studies and explores its paths forward. We begin by examining the intellectual origins of the approach and by defining why and how it matters to the study of management and organizations. Specifically, we emphasize the performative role of history in making and unmaking organizational orders. Next, we elaborate on how the articles in the special issue demonstrate the uses of the past in shaping organizational identity, strategy, and power. We also highlight how this work contributes to our understanding of the socially embedded character of history in organizations by accounting for the role of materiality, intertextuality, competing narratives, practices, and audiences in how the past is used. We conclude by considering four research frontiers particularly worthy of further exploration—the influence of temporal form, the role of non-rational knowledge, the range of methods, and the integration of ethics—in studies of the uses of the past in organizations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.008 | 0.062 |
| Scholarly communication | 0.018 | 0.026 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".