Chain-computerisation and theories on knowledge-sharing: Prevent a fallacy of the wrong level and do not step into the pitfall of an unstructured-information overload
Bibliographic record
Abstract
This article is available in English and DutchChain-computerisation is, as always, still a topical subject and the demand for more insights and tools is steadily increasing. These can be obtained from other areas of research, but caution should be exercised when applying them to social chains. A fallacy of the wrong level is easily made and you can't structure an unstructured-information overload so easily. In my Master’s thesis, I identified aspects that can be used to assess the usability of insights and tools from other theories in the context of large-scale social chains. As far as I know, the only knowledge theory that pays explicit attention to – or is based on – scale is that of Nonaka.The insight that knowledge-sharing is essential for chain co-operation (focused on dealing with the dominant chain problem) emphasizes that the core of chain-computerisation is all about communication and not about simply compiling files. A dominant chain problem ensures the necessary focus and selection with respect to the exchange of information and the knowledge to be shared at the level of the chain as a whole. A standard solution does not often provide the chain communication that is necessary for dealing with a specific dominant chain problem; a chain-specific solution is generally necessary.
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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 | 0.020 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.060 |
| Scholarly communication | 0.013 | 0.052 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".