Communication as organizing: Empirical and theoretical explorations in the dynamic of text and conversation
Bibliographic record
Abstract
book, The Emergent Organization (2000), as “one of the most impor-tant books about organizations to be published in the last 20 years” (Weick, 2001, p. 168). Communication as Organizing, a new collection of articles by various authors in what they now style the “Montréal School, ” continues from that work. The self-identification of the editors and authors of this collection as the Montréal School is itself of interest. English is the first language of only three of the ten authors, and two of these three teach at non-English-speaking institutions. The authors come from seven different countries, and although three received PhDs from American universities, none were born in the U.S. and only one now teaches there. Taylor’s own bilingual ability is evident from his writing. Brumann—one of the authors with a U.S. PhD—suggests that the school links European and American pragmatism (p. 203). While reading this book, it is interesting to reflect on the organization and communication path-dependencies that brought about this particular configuration. Although as a collection of chapters this book does not have the same impact as the earlier work, it develops important themes of agency and complementarity that I will
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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.006 | 0.023 |
| Scholarly communication | 0.015 | 0.032 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".