Behind the scenes with genomics researchers
Bibliographic record
Abstract
Although lab-coat genomics scientists are highly skilled and involved in pioneering work, few studies have examined their perceptions on what they do, and how they relate with others in interdisciplinary work. Recognizing that gap, we were curious to talk with scientists about their current work and positionalities related to the use of genomics for bioremediation. Using unstructured open-ended interviews and thematic analysis, we interviewed researchers with diverse genomics-related expertise. Emerging topics were grouped into two broad categories akin to Bronfenbrenner's nested developmental model: microsystem matters, comprising technical advances, barriers, and localized concerns; and macrosystem matters, exploring wider reflections and the philosophies of genomics and society. At the microsystem level, findings revealed differences of opinion about methodological steps, but there was agreement about the incompleteness of databases and the absence of established reference values. These two problems may not only impact a project's progress but also the ability to gauge success, affecting budgeting, human resource needs, and overall stress. At the macrosystem level, scientists voiced concerns about how different social groups perceive and accept genomics applications, as those tend to be viewed by lay persons as genetic interventions. Another focus was on how academic publication slows progress because it is orientated toward positive results while gaps in knowledge could be filled by publishing negative results or methodological barriers. This study underscores scientists' self-awareness within the genomics discipline, acknowledging how their beliefs and biases shape research outcomes. It illuminates critical reflections essential for navigating societal and scientific landscapes in genomics research.
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 | 0.096 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.039 | 0.059 |
| Scholarly communication | 0.024 | 0.027 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.017 | 0.041 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".