Autopietic decisions approach: a governance research network case study
Bibliographic record
Abstract
Purpose – The purpose of this paper is to explore the autopoietic decisions approach ( Design/methodology/approach – The approach selected was Luhmann's Social System Theory, an autopoietic decisions system. A historical case study was reconstructed in which information was recollected by in-depth interviews and a survey. The network results, the extensive communications submitted by the members of two network congresses (2006 and 2010) were analyzed by networks analysis techniques. Findings – The approach and model developed were useful to identify the decision premises, which have been the constitutional structure of the research network. Practical implications – Development of a governance approach useful to a research network organization which retro-feeds the quality movement guidelines. Originality/value – The quality movement proposes a systematic regulatory approach, via the ISO9000 standard family. This approach has not sufficed for institutions of higher education. One of the reasons is that it favors the “management of things” from a processes standpoint, which conforms to the General Systems Theory. However, the core of higher education is not “things” but rather the “people” participating in it – particularly professors, students, and the university community – who are participating in the creation, teaching, association, and diffusion of knowledge. The unsolved problem refers to governance.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".