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Record W2619226989 · doi:10.1353/llt.2017.0017

Decolonizing Employment: Aboriginal Inclusion in Canada's Labour Market by Shauna MacKinnon

2017· article· en· W2619226989 on OpenAlexvenueaboutno aff
Jonathan Clapperton

Bibliographic record

VenueLabour / Le Travail · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsInclusion (mineral)WorkforcePovertyPolitical scienceEconomic growthSociologyEconomicsGender studies

Abstract

fetched live from OpenAlex

Reviewed by: Decolonizing Employment: Aboriginal Inclusion in Canada's Labour Market by Shauna MacKinnon Jonathan Clapperton Shauna MacKinnon, Decolonizing Employment: Aboriginal Inclusion in Canada's Labour Market (Winnipeg: University of Manitoba Press 2015) Shauna MacKinnon's Decolonizing Employment: Aboriginal Inclusion in Canada's Labour Market is a timely publication, roughly coinciding with the release of the Final Report of the Truth and Reconciliation Commission of Canada (trc). Moreover, the trc's Calls to Action include items which Decolonizing Employment directly speak: Aboriginal education, training, and employment. MacKinnon's book asks why training and education initiatives and economic policies have not led to significant improvement in the overrepresentation of Aboriginal people measuring poorly on social and economic indicators. She does so by analyzing the impacts of the neoliberal approach to labour market policy and a number of training initiatives in Winnipeg. MacKinnon comes to the conclusion that supply-side (neoliberal) strategies aiming to train individuals quickly to meet market demand are far less effective at – and can hinder – addressing labour inequities and responding to the needs of Aboriginal individuals and communities than demand-side strategies. MacKinnon continues that both strategies can "play an important role in expanding access to the workforce," (182) but contends it will not be solely training programs that will end poverty and exclusion but other "factors that make a real difference in people's lives." (182) Through its attention to multiple geographical scales – international, national, provincial, and municipal – Decolonizing Employment provides a valuable contribution to the existing dialogue on Aboriginal people, the market, education, training and, as the title aptly describes, decolonization. The book is divided into two parts. Part One, "The Political Economy, Labour Market Policy, and Aboriginal People in Canada," appraises the use and effectiveness of current economic and social policies as tools to address poverty, inequality, and social exclusion before turning to "the changing nature and effectiveness of labour market policy, and in particular training policy, as the primary tool currently being used by governments to address the poverty and social exclusion of Aboriginal people in Canada." (3) MacKinnon is especially attentive to, and critical of, the rise of neoliberal policies in Canada, identifying their origin to the early 1980s and Prime Minister Brian Mulroney (this a common misperception given some of the economic policies of Prime Minister Pierre Trudeau in the later years of his party's rule that were arguably neoliberal). MacKinnon then describes the ongoing economic disparity between Aboriginal and non-Aboriginal peoples. Part Two, "The Case of Manitoba," comprises roughly three-quarters of the book's length. It describes "how the integration of decolonizing programming has shown to be effective but difficult to implement in a neoliberal policy environment." (3–4) Chapter 3 is a poignant critical analysis of Manitoba's labour policy over the past couple of decades concerning social programming, intervention into the economy, and policy as it targets and affects Aboriginal people, while Chapter 4 provides a brief sketch of a decolonizing framework for training, including decolonizing the classroom and of course curriculum content. The next three chapters, which focus on case studies of training and education programs in inner-city Winnipeg, are the most compelling in the book. Using a mixture of quantitative and qualitative data, they provide greater analysis of the [End Page 273] neoliberal training model and its limitations for Aboriginal people in action, demonstrating that the model's narrow focus on training individuals to meet the requirements of the private sector in a compressed timeline disregard the complicated effects of colonization, the lack of "good" jobs available, the inability of short-term training to prepare individuals for those jobs available, and systemic issues such as discrimination and racism. MacKinnon divides the training institutions she uses for cases studies into three categories, each of which she critically appraises: neoliberal; supply-side initiative programs less aligned with neoliberalism because they are long-term and participants are awarded a university degree at the end of the program; and, initiatives that address both supply and demand. MacKinnon also analyzes the information she gleaned from 36 interviews with students, teachers, counsellors, and program managers. Chapter 7 is arguably the most captivating as it is...

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.072
Threshold uncertainty score0.523

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.006
Science and technology studies0.0160.008
Scholarly communication0.0090.004
Open science0.0030.004
Research integrity0.0050.010
Insufficient payload (model declined to judge)0.0110.003

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 head0.008
GPT teacher head0.246
Teacher spread0.239 · 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 source (direct Gemma or distilled Codex), 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
Published2017
Admission routes2
Has abstractyes

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