Teaching Open Science: Published Data and Digital Literacy in Archaeology Classrooms
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT Digital literacy has been cited as one of the primary challenges to ensuring data reuse and increasing the value placed on open science. Incorporating published data into classrooms and training is at the core of tackling this issue. This article presents case studies in teaching with different published data platforms, in three different countries (the Netherlands, Canada, and the United States), to students at different levels and with differing skill levels. In outlining their approaches, successes, and failures in teaching with open data, it is argued that collaboration with data publishers is critical to improving data reuse and education. Moreover, increased opportunities for digital skills training and scaffolding across program curriculum are necessary for managing the learning curve and teaching students the values of open science.
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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 | 0.014 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.005 | 0.363 |
| Open science | 0.021 | 0.075 |
| Research integrity | 0.000 | 0.001 |
| 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 it