SciSpends: an exploratory survey investigating nonreimbursed expenses in biological sciences
Bibliographic record
Abstract
Socio-economic barriers to participation in science are harmful to the enterprise. One barrier is the phenomenon of scientists paying for expenses related to the conduct of research that are not reimbursed (which we termed “Scispends” for social media discourse). We conducted an online survey that asked self-selecting respondents to report the amount of money they spent on nonreimbursed expenses, including both costs incurred in the past 12 months and one-time startup costs associated with their current position. We received 857 responses that met criteria for inclusion and reported descriptive statistics summarizing nonreimbursed expenses across career stages. We found the median total of nonreimbursed expenses for the past 12 months was $1680 and the median one-time expenses were $2700, and that as a proportion of income these expenses were highest for those earliest in their careers. We found 13% of respondents spent more on unreimbursed expenses than they earned in a year. All cost categories were skewed, with most respondents reporting little or no expense, whereas a minority experienced high expenses. These results should be interpreted with caution due to survey’s exploratory design, but they suggest that formal surveys should be conducted by scientific societies, funding agencies, and academic institutions to properly assess the causes of nonreimbursed expenses and determine how this barrier can be minimized.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".