All Hands on Deck: Coordinated approaches to Indigenous early career development
Bibliographic record
Abstract
The Developing Indigenous Early Career Researchers Project is a three-year longitudinal study funded by the Australian Research Council investigating the experiences and perspectives of Indigenous early career researchers working in universities across Australia. In an earlier paper we explored self-identified needs of Indigenous early career researchers regarding the development of sound research trajectories and careers in the academy (Locke et al., 2022). This paper takes a step further in investigating, who is responsible and should be held to account for supporting the development of these Indigenous early carer researchers. Data collected from across all three stages (2020, 2021 and 2022) of this project suggests that Indigenous early career researchers consider that all university staff, including themselves have certain responsibilities towards developing their academic career trajectories. Some Indigenous ECRs also pointed out the roles that external agencies such as government and funding bodies play in guiding institutions to value and promote the diversity and wealth of knowledge that Indigenous academics bring to the academy. This paper engages an Indigenist research approach and employs relatedness theory in advocating the development of policies and programs to support the career trajectories of Indigenous Early Career Researchers, from an Indigenous epistemological standpoint. To close, this paper posits that to achieve optimum outcomes for Indigenous ECR’s there needs to be systematic and coordinated institutional approaches to developing career trajectories that ameliorate challenges and barriers to Indigenous ECR career progression.
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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 | 0.069 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.029 | 0.022 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.006 | 0.045 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".