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The Impact of Massification - Is Australian Education Moving towards Deterioration - A Study

2022· article· en· W4285241228 on OpenAlexaff
Joanna Claire Miranda, James Sillitoe, Jo Vu

Bibliographic record

VenueSplint International Journal Of Professionals · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicEducation Systems and Policy
Canadian institutionsQuest University Canada
Fundersnot available
KeywordsPolitical science

Abstract

fetched live from OpenAlex

The aim of this study was to explore the impact of removal of higher education funding to the delivery and quality of Australian higher education. The data for this paper was collected from academics employed at Institutes of higher education within the Melbourne area in Australia. As the purpose of the study was to gain in-depth information from academics, a qualitative approach was taken. Academics from Institute of higher education were interviewed one on one. The data was analysed using Nvivo to identify themes. There are important contributions that surfaced out of this study, through this study, (i) the factors that are perceived as lowering the quality of higher education was identified. Through that identification, (ii) further investigation and corrective actions can now be taken to ensure that the quality of education is not compromised and the sustainability of these Institutes of Higher Education can be safeguarded. This study also provides recommendations as to what can be done to support Institutes of Higher Education to meet teaching and research goals.

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.

How this classification was reachedexpand

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.127
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.086
GPT teacher head0.518
Teacher spread0.432 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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