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Record W6981641240

Evaluating approaches to EAL newcomer support: protocol for a systematic review.

2022· dissertation· en· W6981641240 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueOxford University Research Archive (ORA) (University of Oxford) · 2022
Typedissertation
Languageen
FieldComputer Science
TopicAdvanced Multi-Objective Optimization Algorithms
Canadian institutionsnot available
Fundersnot available
KeywordsMainstreamVariety (cybernetics)Inclusion (mineral)TrustworthinessProtocol (science)Qualitative researchMainstreamingNarrative
DOInot available

Abstract

fetched live from OpenAlex

<p><b>Background</b></p>\n<p>There has been an influx of students in the United Kingdom, Australia, Canada, and Ireland who arrive in the country without adequate English skills to fully participate in school. Governments and schools utilised a variety of methods to cater to these newcomer students. Across the aforementioned countries, numerous different approaches to English as an Additional Language (EAL) provision. These can be best categorised as placing newcomers in the mainstream classes (mainstreaming model) or withdrawing them for separate, isolated provision (withdrawal model). Research is lacking on the relative effectiveness of these two different models of newcomer EAL provision.</p>\n\n<p><b>Objectives</b></p>\n<p>This systematic review seeks to uncover the nature and extent of research on newcomer EAL provision in the United Kingdom, Australia, Canada, and Ireland. It aims to provide commentary on the relative effectiveness of different approaches and to determine whether policies aimed at newcomers are supported by solid evidence.</p>\n\n<p><b>Methods</b></p>\n<p>This review uses a best-evidence, systematic review approach to answer the research questions. The search string identified 3,332 records. Using pre-defined inclusion and exclusion criteria the studies were screened for suitability. I evaluated the trustworthiness of each study based on study design and reporting practices.</p>\n\n<p><b>Results</b></p>\n<p>The review found 15 eligible studies, with six in each of Australia and the United Kingdom, two in Ireland and a solitary study conducted in Canada. Qualitative data was reported in each study, so a narrative synthesis was used. Researchers primarily conducted ethnographic studies to investigate how mainstreaming and withdrawal impacted newcomers and their emotional, social, and academic development. No records had a study design that was able to directly assess effectiveness of approaches, with comparisons between the two models non-existent. From observations and interviews with EAL stakeholders, researchers reported positive and negative aspects of both mainstreaming and withdrawal approaches. Mainstreaming was predominantly found to be exclusionary and academically unhelpful for newcomer students. Conversely, withdrawal approaches offered some tangible benefits for newly arrived students.</p>\n\n<p><b>Implications</b></p>\n<p>For researchers, this study shed a light on the lack of solid evidence which had empirically assessed the efficacy of approaches to newcomer EAL provision. Further research is desperately needed in this field as the current state of the field has resulted in nebulous, fragmentary, and inconsistent. With regards to policy, governments have not listened to the little evidence that does exist and instead have made policies in reaction to social movements that have little evidence to support them. The implications for policy makers and practitioners are therefore clear: re-evaluate current approaches to EAL provision and follow research suggestions as to the most effective model. \n</p>

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.651
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0050.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.142
GPT teacher head0.380
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it