Bibliographic record
Abstract
This article addresses Francisco Varela’s epistemological ideal of presencing mastery. For some time, further clarification has been needed to explore its feasibility, the methodological means to it as well as its cultivation in contexts of practice. As a response, drawing from the work of Dynamic Presencing (Gunnlaugson 2020, 2021, 2023; Gunnlaugson and Brendel, 2019, 2020, 2021), I introduce a three-stage ontological process-method for cultivating presencing mastery by developing one’s presencing self from the ground of presence in the presencing field. In contrast to Varela’s ideal, this ontological method serves as an internal, subtle yet robust scaffolding structure, that when applied as a transformative method, introduces a new form of presencing mastery as a generative way of being in leadership and coaching contexts.
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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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.008 | 0.042 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.009 |
| 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".