Plus Ça Change: The Persistence of ‘Skill Talk’ in Competency Discourse
Bibliographic record
Abstract
Robin Barrow has critiqued the use of the concept of “skill” for a wide range of human attributes that are not skills in the precise sense he articulated, namely: “a capacity that is discrete and can be perfected through practice and exercise.” Skill talk has persisted, though today commonly under the guise of “competency discourse.” In recent years, the British Columbia Ministry of Education has implemented a new K-12 curriculum that relies heavily on “communication competency,” “thinking competency” and “personal and social competency.” Based on Barrow’s work, I critique the tendency to refer to a wide range of human qualities as “competencies.” In addition, I argue that competency discourse commits a category mistake, especially with respect to moral qualities in the “personal and social competency” domain. After taking a closer look at the area of “personal and social competency” in BC’s new curriculum, I discuss the concept of friendship as an example of an area of significance to human life that cannot be reduced to competency. I close the paper by discussing why it matters that competency discourse commits a category mistake, and why philosophers of education should resist competency discourse.
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 | 0.018 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.081 |
| Scholarly communication | 0.016 | 0.020 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.007 | 0.016 |
| 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".