Communicating primary school enrolment information to parents from non-English-speaking backgrounds
Bibliographic record
Abstract
This thesis investigates how enrolment and other important information on primary schools’ websites in metropolitan Sydney is communicated to parents from non-English speaking backgrounds. Education is crucial for integrating migrants’ children into a host society (Portes & Rumbaut, 2014), and parental involvement in schooling is strongly correlated with educational success (Lareau, 2000). However, for migrant parents to be involved in a meaningful way, they need linguistically accessible schooling information. In Australia, almost half (49%) of the population was either born overseas or has at least one parent born overseas. This has resulted in significant linguistic diversity, with 22.2% of the population reporting they speak a language other than English at home, and 16.6% of this group not speaking English fluently or at all (Australian Bureau of Statistics, 2017; Piller, 2018a, 2018b). Research has shown that educational attainment can be a powerful predictor of later occupational and financial success (Lareau, 2000, 2011; Portes & Rumbaut, 2001). The literature also shows that the lower parents’ socio-economic status, the harder it is for them to be involved in their children’s education (McCrory Calarco, 2018). As migrant status often coincides with low socio-economic status in metropolitan Sydney, education becomes particularly crucial to children of migrant parents and their integration and upward social mobility. The present study examines the issue of linguistic diversity and migrant integration using critical discourse analysis and thematic analysis of 30 linguistically and socioeconomically diverse primary school websites in metropolitan Sydney. It addresses the question of whether schools tailor their communications to accommodate the different linguistic demographics of their communities. The findings show a ‘blindness’ regarding linguistic diversity in that English is the primary means of communication, even when schools acknowledge that their student population is linguistically diverse. When translations are provided, a mismatch between the languages provided and the demographics of school communities can be observed. The findings illustrate how the monolingual mindset of the multicultural school found in previous studies (Ellis, Gogolin, & Clyne, 2010) is prevalent in the online discourse of linguistically diverse schools in Sydney. By comparing schools’ linguistic choices to the community’s linguistic demographics, this study provides crucial information regarding the efficacy of schools’ online communication with linguistically diverse parents. These findings have the potential to contribute to more equitable linguistic policies. For instance, they can help create linguistically inclusive schooling information that bridges the divide between the translations currently available on schools’ websites and the languages spoken by communities. Rectification of these linguistic discrepancies will help ensure all students receive equal educational opportunities by providing equitable access to schooling information, thereby giving them the best chance of later success in life.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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; 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".