Theorizing from the Phenomenon of Addiction
Bibliographic record
Abstract
Full symposium title: Theorizing from the Phenomenon of Addiction-- Opportunities and Challenges for Organization and Management Research. We showcase three projects focused on the phenomenon of addiction but each from a different theoretical angle. Doing so, we aim to bring out the complexity inherent in addiction-related topics and the generative ability of this phenomenon for theorizing and consequently informing real-world practices. The presentations and discussions in this symposium can speak to the missions mandated in the divisions of OMT, HCM, and PNP. Rhythm of Reliability – On Temporality in Relational Work in Addiction Counseling Author: Magdalena Waeber; University of Bern Author: Emamdeen Fohim; University of Bern Author: Claus D. Jacobs; University of Bern Banking the Unbanked: The Case of Integration of a Social Enterprise in a Larger Non-Profit Author: Asma Zafar; Brock University Author: Sylvia Grewatsch; Our Reality Also Matters! Audiences’ Lived Experience of the Stigmatized Practice Author: Emma Lei Jing; NEOMA Business School Author: Rongrong Zhang; The Chinese University of Hong Kong, Shenzhen
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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.050 |
| Scholarly communication | 0.008 | 0.018 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| 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".