Towards family-friendly conferences in the geosciences: results from a first survey
Bibliographic record
Abstract
Abstract. In the geoscientific field, building an academic career requires a high level of dedication to research, frequent publishing, and maintaining visibility within the academic community. Conferences are the central platforms for networking and knowledge exchange, making them keystones for a scientific career. However, the early stages of an academic career often coincide with family planning considerations. Many young researchers face the challenge of balancing professional responsibilities with family obligations. A balancing act that becomes particularly difficult during non-routine work events such as conferences. Such occasions tend to pose significant organizational challenges for families. As mothers still perform most caregiving, they are disproportionately affected. This conflict is an important factor inhibiting the rise of women through the academic career ladder. Hence, increasing the family friendliness of conferences holds a chance of diversifying the community, allowing more women to participate, while reducing the tension of the academic work-family conflict. In this work, we present the results from an online survey among researchers from the geoscientific field. The required support of parents at conferences is analyzed and the acceptance of researchers without children for the implementation of new support offers is examined. Our key findings are that (1) parents wish for more support at conferences, (2) the majority of non-parents welcome family-friendly measures and (3) conference organizers can accomplish a lot in this respect with small adjustments. The responses we received from parents paint a qualitative picture of measures that can be taken to create a family-friendly conference experience, ranging from an increase of transparency and awareness for the challenges of parents to tangible offers like childcare and financial funding. This feedback invites conference organizers to join the conversation about family support and shows that new measures have great support in the geoscientific community. Our findings are distilled into guidelines for conference organizers, allowing them to better address the work-family conflict and transform their conferences towards more equity, diversity and inclusion.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".