SEARCHING FOR TASKS: TASK-ORIENTATION AND THE PROCESSUALITY OF DIGITALSKILLS
Bibliographic record
Abstract
Skills are not “out there” to be learned but develop through their enactment in situ. Rather than defining skills using a tool- or media-driven approach, we understand skills as situated, embodied processes known as ‘task-orientation’. Coined by Tim Ingold (2011, p. 195) this term refers to “any practical operation, carried out by a skilled agent in an environment as part of his or her normal business of life”. Part of this approach entails taking a holistic, narrative approach to investigating everyday life (Ingold, 2000; 2006; 2011) which some scholars have applied to digital media use (Moores 2017; Sumartojo et al. 2016; Pink 2016, 2015; Pink & Leder Mackley 2013; Pink 2011). We believe task-orientation offers a flexible way to define tasks performed online and offline encompassing three interrelated themes: 1) the processional quality of tool use; 2) the synergy of practitioner, tool, and material; and, 3) the coupling of perception and action. Our focus for this project is an investigation into how people perform unfamiliar tasks with digital media using the four processional phases. Unfamiliar tasks are an inherent part of our digitally-mediated everyday life to the extent that we have learned to ‘cope’ (see Sigaut 1994; see Ingold, 2000, p. 332; see also Nicolosi & Falsaperna, 2015, p. 71) with them — making them, in turn, one of our most ubiquitous and essential digital skills.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".