Bibliographic record
Abstract
This short paper (and Pecha Kucha presentation) explores new mobilities and spatial re-orderings of adult work-learning practices. Attention is given to the more sophisticated digital fluencies that seem to be demanded of adult work-learners and the pedagogical implications for educators. Sociomaterial perspectives encourage thinking about how “thingly gatherings” serve in the performance of practice. The unbounded blurry nature of the web and its artefacts can perhaps be described as fluid spaces enfolding with other fluid spaces. Thus, web-based spaces are not containers in which online learning activities take place but rather sociomaterial assemblages that take on particular energies as people and things—both online and offline—negotiate how they move, mix, and mobilize in their correspondences. Analysis draws on empirical data from a research project that explored the effects of the infusion of web and mobile technologies in the enactment of the global work and everyday learning practices of the contingent workforce (the self-employed or micro-small business entrepreneurs). An array of mobilities became evident in these practices, including interactions that slide in, through, and between different cyberspaces; the persistent infusion of the digital and physical into the other; and often capricious and vacillating patterns of presence and absence. However, alongside the mobilities that become evident in these practices, immobilities were also prominent. Using the sociality of practices around mobile devices as an entry point to explore this contradiction, it seems that forces and flows of mobilities are also tied to specificities of place. Although the physical becomes entangled with the digital to enact a specific work-learning space, such spatial re-orderings are not always easily accomplished. Moreover, the often overlooked and invisible spatial negotiations evoked to enact mobility unfold in multiple work-learning places: at home, on the move, in third spaces, at the office, field-based temporary work sites, and innumerable online spaces. This multiplicity adds complexity to how work-learning spaces are conceptualized. Several digital fluencies (a mix of expertise, responsibility, criticality, and innovation) emerge, urging pedagogical and policy response. Four will be highlighted: navigating scale, negotiating openness, wayfinding (Siemens, 2011), and fragmenting-tethering. How to work through the challenges of addressing these fluencies and how best to interrupt current practices are questions facing both educators and adult worker-learners and I hope this paper prompts such discussion.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.065 | 0.024 |
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".