Legitimacy Without a Past: Two Complementary Historical and Legitimizing Frames
Bibliographic record
Abstract
Decades of research have shown the salience of legitimacy for organizations, with history emerging as a critical resource for legitimacy claims. Organizations can leverage their past to bolster their image, refashion their identity or shape the course of their future. Yet, not every organization or sector possesses, or is embedded in, a long history that can be mobilised towards a specific end. We know little about how such organisations mobilise history and the past in the pursuit of future-oriented (forms of) legitimacy. Here, we ask: How do organizations operating in a sector without an extended history position themselves vis-à-vis the past when articulating and legitimizing their future? We examine this question in the context of the coworking movement. Drawing from interviews with senior managing staff in coworking spaces located in four global cities, we explore how the past and history are mobilised in the articulation of future-oriented legitimising strategies for coworking spaces. We show that this process relies on five temporal narratives – Historicizing, Detaching, Bracketing, Anchoring and Projecting – each connected to their own legitimacy claims. These narratives translate into two complementary historical and legitimizing frames that are weaved together to articulate a legitimate vision of the future of coworking spaces.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.013 | 0.078 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".