Addressing the tacit knowledge gap in knowledge systems across agential realism
Bibliographic record
Abstract
Past literature has claimed that knowledge systems can enhance or facilitate the creation, retention, transfer and application of tacit knowledge. While this paper agrees that this objective is realizable, it argues that the literature has so far failed to successfully operationalize this, since at the core of the models published to date lies the flawed epistemological assumption of knowledge ‘conversion’ – more specifically, tacit to explicit knowledge conversion. \ \ This paper proposes the alternative epistemology of agential realism which allows us to reframe tacit knowledge within knowledge systems, whereby humans and machine are coupled together (intra-act) to enhance and retain tacit knowledge creation and sharing without putting undue emphasis on its conversion and storability into an explicit form – thus, agential realism allows tacit to remain as tacit. In addition, this critical-conceptual paper proposes nascent examples of human-machine or knowledge system configurations which have affinities or potential affinities with an agential realist approach. \
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.044 |
| Scholarly communication | 0.014 | 0.025 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.006 |
| 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".