Autopietic decisions approach: a governance research network case study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose – The purpose of this paper is to explore the autopoietic decisions approach (<Greek: autos=self, poiein=to produce) means self-(re)production and to know the constitution of the governance in the organization of a research network. 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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.068 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it