Evaluative Frameworks and Scientific Knowledge for Undergraduate STEM Students: An Illustrative Case Study Perspective
Bibliographic record
Abstract
COVID-19 gives an important focal point to the increasingly complex and overwhelming amounts, types, and availability of information undergraduate STEM students are faced with. The world at large is being asked to seek information around serious infectious diseases and find information that can help facilitate decision-making in both personal and academic settings. Much of the available information lacks a fundamental scientific basis but is often masquerading as ‘truth’. This is translated both into how society at large seeks information to make decisions, as well as how STEM undergraduate students are finding information to build their scientific skill set. This paper uses two case study examples of publications in scientific journals to examine the concept of using RADAR to determine validity. STEM librarians should focus on using evaluative frameworks as an initial launch point for critique, but a conversation must begin around how to encourage student realization of broader context and specifically awareness of what is still unknown.
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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 | 0.044 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.015 | 0.021 |
| Scholarly communication | 0.018 | 0.010 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".