Cultivating a more effective culture to advance the engineering of microbial communities
Bibliographic record
Abstract
This theme issue holds contributions from a diverse group of individuals and research groups all dedicated to applying the power and principles of microbial ecology to create the environmental biotechnologies needed in the twenty-first century.These people came together in March 2022 at a Royal Society Theo Murphy meeting to discuss the matter.This is a vibrant field with many opportunities, challenges and barriers.In the final session of the meeting, we had participants break into small groups and asked them to discuss how we could accelerate progress.Progress to develop the new technologies that would help us to solve some of the grand challenges that humanity currently faces.To our surprise, every single group identified the culture of academia as a key issue impeding progress.We learnt that our culture prevents successful cross-disciplinary collaboration.We learnt that the competitive nature of research environments and the lack of inclusivity make us less than the sum of our parts.We heard how the reward structure of academia perversely incentivizes those activities and behaviours that hamper successful trans-disciplinary collaboration.Of course, such a diverse group brought a variety of experiences to the discussion.Some were fortunate to have experienced supportive, collegiate and creative cultures.Others less so.Nevertheless, even the most fortunate of us were touched by the unpleasant consequences of the pervasive rules of the academic game.In order to move faster and more effectively as a field, we need to build a new culture that is focused on collaboration and better solutions rather than one that is centred on competition and metrics.What then is our culture?It is simply the values, norms and behaviours that we espouse as a community.The culture of microbial ecologists and engineering biologists naturally reflects those of our societies and the demands and rewards of our employers and employment, and thus much of twenty-first century science.However, there is no reason to assume or accept that the culture that spontaneously arises is the culture we want.Indeed our colleagues have made it abundantly clear that we do not have the culture we need or desire.We envision a culture that allows all of us to have fulfilling and enriched research careers and to meet the very real societal challenges that we face.The clarion call from our confederates is a clear confirmation that we need to do better in both the quality and the effectiveness of our research environments.We argue that the two are intimately connected; an improved research culture will not simply bring us more fulfilling careers, but also more effective ones.Perhaps one of the most pernicious ideas in contemporary academia is that an unpleasant culture is somehow more effective at creating progress and societal solutions.We contest that tacit assumption.Recognizing and acknowledging that a cultural change is needed is the first step to change.That being said, people have been talking about the ineffective
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".