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Record W6958755750 · doi:10.7488/era/5901

Free trade agreements through a prism: exploring the making of trade policies from a subcentral government context

2025· other· en· W6958755750 on OpenAlexaboutno aff

Bibliographic record

VenueOpen MIND · 2025
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsJurisdictionContext (archaeology)Government (linguistics)Free tradeNegotiationPublic policyGovernment procurementCommercial policy

Abstract

fetched live from OpenAlex

Free Trade Agreements (FTA) have become more complicated, complex, and progressive with deeper policy integration over time (Capling and Low 2010b; WTO 2011). Where early FTAs addressed at-the-border trade barriers, such as tariffs, contemporary agreements include non-tariff barriers and social policy obligations. Since the 1990s, FTAs started to include non-tariff barriers including labour, environment, transparency, and public procurement measures. These ‘deep’ obligations are found in most modern FTAs. The impact of the growing complexity and remit of FTAs is that they now include policy matters which are managed by subcentral governments and where they have ultimate jurisdiction on these policies, including in Canada and the UK (Melo Araujo 2017a; Kukucha 2015; 2003; Paquin 2013). As a result, these subcentral jurisdictions have been pulled onto the international stage because they have policy jurisdiction over many FTA obligations. Without formal policy levers at their avail, neither a seat at the negotiation table nor a pen to sign with, subcentral jurisdictions are shaping and influencing the outcomes of FTAs, albeit with varying degrees of success. How this is done and which actors are involved in these FTA policy-making processes is not well-known. My research explores this gap by following the making of the Canada-UK FTA negotiation process from the subcentral government context – with the Canadian provincial and UK devolved governments. To do this, I ask: From the subcentral government context, how are FTAs made and in what ways are subcentral government interlocutors involved in these processes? Theoretically, this research is positioned within the post-human new materialist paradigm leaning on Karen Barad’s (2007) Agential Realism and Bruno Latour’s (2005) Actor-Network Theory as the basis for the development of my Prism Approach. This research brings forward a discussion about the material complexities and nuances involved in the creation of these complex legal promises which occur in conjunction with additional becomings (e.g., trade negotiators, teams, and networks) through these cocreated processes. This research incorporated a multi-phased qualitative research design bringing together methods which organically fit with the research question and philosophical underpinnings. The five phases included: policy literature review, interviews with subcentral government policy professionals, series interviews with subcentral government policy professional key informants, participant observation case study of a devolved government trade policy team incorporating a social network analysis component, and two roundtable workshops bringing together the devolved and provincial government trade policy delegations. This research offers contributions to theoretical discussions building on Barad and Latour’s scholarship by reading them diffractively to generate my Prism Approach, and empirically by providing details of the making of an FTA highlighting the reasons why subcentral governments are involved, the processes involved in these negotiations, and an ethnographic case study of a devolved government trade policy team’s network of interlocutors.

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)0.018
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.144
Threshold uncertainty score0.287

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.025
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.006
Science and technology studies0.0120.045
Scholarly communication0.0260.025
Open science0.0030.016
Research integrity0.0040.008
Insufficient payload (model declined to judge)0.0130.001

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 head0.103
GPT teacher head0.320
Teacher spread0.217 · 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 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
Published2025
Admission routes1
Has abstractyes

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