Different Ways of Knowing: An Overview of a Reflective Arts-Based Assignment
Bibliographic record
Abstract
In the fall semester of 2015, third year social work students at an Atlantic University were given an assignment that would have them picking up scissors and paint brushes. They were tasked with creating an arts-based project related to a professional encounter they had experienced within their first practicum placements. The process created a space for students to experience a new way of learning with consideration to Heron’s (1981) theory of extended epistemology and the four ways of knowing: experiential, propositional, presentational, and practical. Some students chose to reflect on interactions with service users, individuals, families, groups, local communities, or interdisciplinary teams, as an example of their experiential knowing. They were asked to focus on an encounter that involved significant power differences with the subject being an area of concern for themselves or those involved. Students then identified a range of factors in their chosen situation, including: the setting and environment; the social and emotional context; their varying perceptions and expectations; and the role of power. These factors connected the theory with the students’ encounters in the field.
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 | 0.021 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".