Adapting a Research Tool for Teaching in a Post-Pandemic World
Bibliographic record
Abstract
The pandemic resulted in significant advances in critical digital pedagogy, including the adaptation of existing technologies as teaching tools. This paper discusses adapting a tool intended for advanced research (Robinson 2023) as a teaching environment. We assess the advantages of this implementation, allowing instructors to pivot from in-person to online learning, as well as the characteristics of evaluation and grading. Urged by the calls of critical digital pedagogy (Stommel, Friend, and Morris 2020) in our paleography course, we took advantage of the potential of digital technologies as the ideal means to promote student autonomy. Such technologies provide a toolkit for students to navigate the learning process independently. This idea is further echoed by Cathy N. Davidson's suggestion to structure a “student-centered class” that utilizes technology to the extent desired by instructors while allowing them to maintain control. This entails empowering students to take an active role regarding their pedagogical needs while fostering a dynamic learning environment which is both flexible and effective (Davidson 2020, n.p.). The spectre of the pandemic and the familiarity of the system within our teaching team made Textual Communities an ideal tool if we could flip its focus from its intended final product, an edition– to one of its initial components –the transcription. Its advantages as a potential distance learning tool and its benefits for correcting and grading are unquestionable. Looking forward, we recommend educators consider similar adaptations of digital tools in their curriculum, keeping in mind that these tools do not overshadow the pedagogical goals but rather enhance and complement them.
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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 | 0.046 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.014 |
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".