Evaluative Frameworks and Scientific Knowledge for Undergraduate STEM Students: An Illustrative Case Study Perspective
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
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.
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.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| 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