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Record W4392161546 · doi:10.46692/9781529222975.004

Mapping the Legal Landscape in Australia, South Africa, Canada and New Zealand and its Applications in the Digital Age

2023· other· en· W4392161546 on OpenAlexaboutno aff
Angelo Capuano

Bibliographic record

Venuenot available
Typeother
Languageen
FieldSocial Sciences
TopicComparative and International Law Studies
Canadian institutionsnot available
Fundersnot available
KeywordsGeographyCartography

Abstract

fetched live from OpenAlex

Introduction The analysis of ILO jurisprudence in Chapter 2 revealed that discrimination in employment based on class and factors that reflect social background is prohibited as part of the prohibition on ‘social origin’ discrimination in ILO conventions, particularly ILO 111 . It also highlighted that the ILO jurisprudence on ‘social origin’ examined in Chapter 2 can, and should, be used to aid the interpretation of the ground ‘social origin’ in the domestic laws of Australia and South Africa. This chapter delves further into the analysis of domestic law, to explore whether and the extent to which discrimination based on class and factors reflective of social background is prohibited in four common law countries. Parts I and II map the legal landscapes in Australia and South Africa respectively, to clarify the concept of ‘social origin’ discrimination in the law of each country and whether this domestic jurisprudence is consistent with the ILO jurisprudence analysed in Chapter 2. The analysis in Part I also includes discussion of other listed grounds within state and/or territory anti-discrimination legislation in Australia. Part III maps the legal landscape in Canada, to clarify the concepts of ‘social condition’ and ‘family status’ discrimination. Finally, Part IV maps the legal landscape in New Zealand, to clarify the concept of ‘family status’ discrimination. The analysis in all four parts of this chapter will show that whilst ‘class’ and ‘social background’ are not listed as grounds of discrimination in legislation within these four countries, the listed grounds just mentioned reflect notions of class and/or factors that go to social background. The analysis will also direct attention to how the law in each country may have certain applications in the digital age. I. The Australian legal landscape and its applications in the digital age Part I of this chapter will map the legal landscape in Australia to explore the extent to which discrimination in employment based on class and/or factors reflective of social background is prohibited at the federal, state and territory level. Whilst discrimination based on ‘class’ and ‘social background’ are not expressly contained within legislation as grounds of discrimination, the below analysis will show that a number of listed grounds of discrimination either include or reflect class and/or factors reflective of social background. It will, further, outline how discrimination based on these grounds have particular applications in the digital age.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.397
Threshold uncertainty score0.169

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.077
GPT teacher head0.308
Teacher spread0.231 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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
Published2023
Admission routes1
Has abstractyes

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