Pipeline Schmipeline: A New Survey to Examine Youth Pathways in Science
Bibliographic record
Abstract
Increasing diversity in science, technology, engineering, and math (STEM) and STEM-related degrees and professions is a national priority. Research on students’ pathways in STEM may contribute to our understanding of how to change institutions to achieve diversity; however, until recently, the dominant narrative invoked a “pipeline” metaphor. In this work, we challenge the pipeline metaphor by interrogating what is meant by a “STEM” pathway, measuring constructs not typically measured in STEM pipeline research, endeavoring to make our measures intersectional, and imagining alternative outcomes in addition to “staying in STEM.” We have been following students who completed an out-of-school mentored science research program since 2017. Three hundred fifty-eight participants responded to an alumni survey designed to collect data about their location along their pathway, constructs related to the pursuit of a pathway, and demographic information. Here, we describe the characteristics of this sample and initial findings about the new constructs we measured. By measuring constructs not typically measured in pathways research and designing items and scales using an intersectional approach, we challenge the problematic pipeline metaphor that dominates the STEM persistence literature.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".