A decade of shaping the futures of polar early career researchers: A legacy of the International Polar Year
Bibliographic record
Abstract
Abstract The Association of Polar Early Career Scientists (APECS) is an important legacy of the International Polar Year (IPY). APECS continues to foster engagement in education, outreach and communication (EOC) activities relating to the polar regions and provide training for early career researchers (ECRs). We highlight opportunities for training, leadership and skills development, such as the annual Polar Weeks and Antarctica Day celebrations. Participation and engagement in EOC activities actively contributes to career development by enabling ECRs to develop valuable soft skills such as networking, communication and interdisciplinary knowledge. A pilot survey on EOC engagement highlighted that those who organise events also gain leadership skills such as team management. We discuss several factors contributing to the success of APECS in training the next generation of polar leaders. These include the geographical rather than discipline-specific focus of the organisation, utilisation of online resources, including social media, and the strong links with partner organisations. These examples demonstrate how the EOC legacy of IPY has continued due to APECS’ targeted efforts to create EOC opportunities and provide skills and leadership training for ECRs.
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 | 0.013 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".