Connecting to Country: A Pathway to Deepening Cultural Understanding
Bibliographic record
Abstract
This project of connecting to country with infants and educators is an act of reconciliation within a community of learners. Through examining our pedagogical and cultural ontologies we journeyed with the children, families and members of the Awabakal community respectfully engaging with ancestral language, knowledge about the land, animals of Mulubinba and the importance of waterways. Due to a global pandemic our ability to wander while learning was constrained to our immediate learning environments. Therefore, we used experiences of digital media, music, art and artefacts as our conveyance to other imaginings within this learning. Where we arrived at is a place of deeply reflective understanding about young children’s capacity to share in the joy of cultural learning and the pathways this creates for whole communities to learn together.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.031 | 0.031 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.002 | 0.029 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 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".