Toward an Autopoietic Approach for Information Systems Development
Bibliographic record
Abstract
Several weaknesses of information systems development methodologies have been identified and studied in the recent years. These weaknesses can be viewed from different perspectives such as: • The characteristics of the outcomes, i.e., information systems: The current methodologies are producing systems with rigid and inflexible that are difficult to maintain and to evolve (e.g., Loucopoulos, 1991). • The degree of the domain-independence: There is a gap between the way system development methodologies in the mainstream of scientific and technical literature and the way they are carried out in real life situations. This is mainly due to the domain-independence of most of these methodologies (Bansler & Bolker, 1993, Vessy & Glass, 1998). • The conceptual and philosophical bases: The dominance of the functionalistic view in the most of current methodologies (Hirschheim, Klein & Lyytinen, 1995, Iivari, 1991, Iivari, Hirschheim & Klein, 1998). In addition, most of information systems development methodologies under-utilize the richness of concepts and insights provided by new and emerging theories such as autopoiesis, self-organization, and fuzzy logic. Moreover, they do not accommodate the new emerging information systems and technologies such as component and framework technologies, web-enabled information systems and ERP.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".