What is NORML? Sedimented Meanings in Ambiguous Organizational Identities
Bibliographic record
Abstract
Organizational identity scholarship has largely focused on the mutability of meanings ascribed to ambiguous identity labels. In contrast, we analyze a case study of the National Organization for the Reform of Marijuana Laws (NORML) to explore how leaders maintained a meaning ascribed to an ambiguous identity label amid successive identity threats. We found that heightened dissensus surrounding meanings attributed to the organization’s “reform group” label at three key points spurred theoretically similar manifestations of two processes. The first, meaning sedimentation, involved leaders invoking history to advocate for the importance of their preferred meaning while mulling the inclusion of others. The second, reconstructing the past, occurred as leaders and members alike offered narratives that obscured the history of disavowed meanings while sharing new memories of those they prioritized. Our work complements research on identity change by drawing attention to the processes by which meaning(s) underlying ambiguous identity labels might survive.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.051 |
| Scholarly communication | 0.013 | 0.020 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".