Bibliographic record
Abstract
Organisations increasingly accept that a genuine commitment to workforce diversity and inclusion improves profitability, reputation and effectiveness. It is also widely accepted that natural CSG projects on the eastern seaboard face increasing challenges from community opposition groups and regulatory change. Embracing diversity and inclusion in the workplace, and developing CSG projects, both require authentic engagement. AGL Energy Limited’s (AGL) Inclusion and Diversity Policy recognises that a diverse workforce, with its broad range of experience and perspectives, has a better opportunity to understand and engage in AGL’s customer base and the communities in which it works. AGL’s policy also emphasises how a diverse workforce can facilitate more creative, innovative and effective solutions. This extended abstract considers how workplace diversity can positively contribute to the development of CSG projects, which must navigate organised community opposition as well as complex regulatory environments. CSG projects, which are typically located in regional areas, can also positively contribute to a more diverse workforce. Provided are examples of situations in which diversity of—and respect for—skills, experience, gender, age, and backgrounds have assisted in achieving successful access negotiations, and enabled authentic engagement with members of the communities in which AGL operates.
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 | 0.022 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.020 | 0.041 |
| Scholarly communication | 0.021 | 0.017 |
| Open science | 0.003 | 0.045 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 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".