StandICT Webinar_Women in ICT Standardisation, third edition - Slides
Bibliographic record
Abstract
The third edition of the Women in ICT Standardisation Webinar presented by StandICT.eu, in coordination with 5 other EU-funded projects dedicated to accelerating European standardisation activities, addressed the key priorities outlined in the 2024 publication of the European Commission’s Annual Union Work Programme for European Standardisation (AUWP). The online session brought together leading women voices in the field of European standardisation to reinforce the relationship between standards, technology, and policy, while highlighting the gender gaps in the field of ICT standardisation and 8 policy priorities identified under the AUWP. The speakers were selected based on their contributions to the development of standards across a variety of ICT domains, in addition to being ambassadors of diversity and inclusion in the ICT standardisation space. This webinar was not only an opportunity for women to demonstrate expertise in their specific discipline. It provided a platform to share personal experiences about being a woman working in science, technology, engineering and mathematics (STEM), and captured insights and best practices to empower inclusion and promote equity in this male-dominated field.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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; both teacher heads agree on what is shown here.
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".