Literature Mapping: A Scaffolded, Scalable, Low-Overhead Undergraduate Research Experience
Bibliographic record
Abstract
There is a wealth of evidence that involving undergraduate students in research has positive impacts in a variety of areas, from representation and retention to outcomes and self-efficacy. However, developing and growing an undergraduate research program can be daunting, especially for institutions that do not have a large existing research enterprise. In this work, we detail a program that revolves around student-developed literature maps to help students gain the ability to read and assess research papers in a way that is accessible, robust, and requires relatively little faculty overhead. We further detail how this program has been run through 4 iterations, with a total of 47 students producing 5 posters or short papers, and 3 full papers. In this work, we provide our experiences using literature mapping projects to boot-strap an undergraduate research program and provide quantitative and qualitative analysis of the students who have participated. All of the materials, including sample spreadsheets, and scripts to generate LaTeX tables and figures are included for anyone wishing to undertake a literature mapping project of their own.
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 | 0.030 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 0.015 |
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".