The Dialogue: an Essential Component to Consider “Organization as a Community of Persons”
Bibliographic record
Abstract
Even if the concept of “community of persons” is more and more present, only a few studies with practical cases actually use it. This paper is based on the pragmatic constructivism epistemological paradigm and uses an autopraxeography method from a previous action research study. The case presented concerns part of a large business. At the beginning of our study, this organization experienced many conflicts. The intervention we implemented developed dialogues and built a community of persons. At the end of this story, a new HR manager arrived and decided to stop what was previously implemented (to increase productivity), putting an end to the dialogues and all of the collaboration that went along with them. This way of doing leads to fake productivity because it generates a lot of hidden costs. Thus, this case shows that a dialogue is necessary to build a community of persons, and vice versa. Otherwise, there are just associations of individuals.
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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.014 | 0.082 |
| Scholarly communication | 0.016 | 0.031 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".