Bibliographic record
Abstract
Purpose The purpose of this paper is to document and analyse the author's first two years of developing a pedagogy that meaningfully incorporates the content creation and social collaboration functions of digital technologies. Design/methodology/approach This paper uses self‐study methodology to describe, interpret, and challenge excerpts from the author's teaching journal to develop warranted assertions for how and why digital technologies are used in particular ways during undergraduate and graduate level courses. Findings One finding of the paper is the relative ease with which the author slipped into a comfort zone of using digital technologies in rather superficial ways, since the major hurdle of ubiquitous access that he had previously encountered was removed due to the fact he was now teaching at a mobile‐enabled institution. The paper also reports on the relative success the author experienced using blogging tools to further develop relationships with undergraduate and graduate students and engage them in meaningful discussions outside of class time. Research limitations/implications Although this study focuses on the author's experiences as a new academic in a mobile‐enabled institution, and thus might initially seem limited in applicability, the practical implications of this research are inherent in the methodology and methods used to critique is pedagogical approach and to challenge himself to find ways to integrate digital technologies into his teaching. Originality/value The paper will be of interest to anyone who has ever struggled with questions of relevancy in the use of digital technologies in both off‐ and online post‐secondary classroom environments.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.008 |
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".