How can school systems weave together Indigenous ways of knowing and response-tointervention to reduce chronic absenteeism in Alberta?
Bibliographic record
Abstract
It is well documented that students who demonstrate high levels of absenteeism are at an increased risk for a number of negative outcomes (e.g., see Fuhs et al., 2018). What is becoming increasingly evident, however, is that students who experience chronic stressors, such as socioeconomic disadvantage, mental health challenges, or cultural marginalization are at an increased risk for school absenteeism and represent specific populations who would greatly benefit from innovative proactive and reactive intervention techniques (Wimmer, 2013). Current Rocky View Schools (RVS) data suggests that of the nearly 800 students who identify as Indigenous within the district, 30% can be considered chronically absent. Data analyzed from September 2017 to April 2018 revealed that on-reserve students who attend an RVS school demonstrated the highest percentage of chronic absenteeism – an alarming 80%. Additionally, these on-reserve students have missed an average of 32 days of school to date this year (representing close to 23% of the school year). Based on the results of the internal data analysis, this study examines the experiences in a public school of First Nations students, who reside on reserve. Interviews were conducted with parents and students and surveys were responded to by staff and what was revealed as a barrier to attendance was a form of cross-cultural anxiety.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".