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Record W2907956284 · doi:10.26481/dis.20181211jk

The Influence of EU Agencies

2018· dissertation· en· W2907956284 on OpenAlexaff
Kim

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicEuropean Union Policy and Governance
Canadian institutionsMontreal Council on Foreign Relations
FundersDirectorate-General for Health and ConsumersDirectorate-General for Migration and Home AffairsEuropean Environment AgencyEuropean Centre for Disease Prevention and ControlEuropean CommissionConsumers, Health, Agriculture and Food Executive AgencyJoint Research CentreEuropean Food Safety Authority
KeywordsTechnocracyEuropean commissionAgency (philosophy)LegitimacyDemocratic deficitCommissionPolitical scienceDe factoEuropean unionPublic administrationCorporate governanceControl (management)BusinessAccountingDemocracyPoliticsEconomic policyEconomicsSociologyLawManagementFinanceSocial science

Abstract

fetched live from OpenAlex

Early Warning and Response System HPA Health Protection Agency JRC Joint Research Centre NGO Non-Governmental Organisation OHIM Office for Harmonisation in the Internal Market PBT Persistent, Bioaccumulative and Toxic PHE Public Health Event Doing a PhD is often described as a long, lonely, and hard journey.It has certainly been a long process.In my case, it has taken longer than the usual four-year PhD period because I became mom two times to my beautiful babies, Mason and Elena.This meant that there were two maternity leaves and two parental leaves along the way, and I was also on long-term sick leave once.Likewise, I would not deny the hard part of a PhD life, involving deadlines, competition, intellectual challenges, stress, and endless writing and rewriting.However, what I can confidently say at the end of my PhD journey is that it has never been a lonely one, thanks to a number of wonderful people who have been there for me in Maastricht, in Eindhoven, and in other parts of the world.First and foremost, I would like to express my sincere gratitude to my two supervisors, Prof. Tannelie Blom and Prof. Esther Versluis.Their course on EU agencies was my favourite in Research Master European Studies and sparked my interest to the extent that I eventually conducted my PhD research on these remarkable entities.Tannelie, thank you for your continuous support for my PhD research, for your useful academic comments and questions, and for your patience.I truly respect the depth of your philosophical thinking and have always enjoyed our discussions during the supervision meetings.Esther, without you I probably would not have been able to finish my dissertation.When I was going through a hard time, you showed me that you believed in my ability and kept encouraging me.Now I know clearlyhow powerful some words of comfort such as "all will be fine" and "everything will work out in the end" can be.Your guidance, encouragement, and willingness to make time (to discuss research and also for coffee to catch up) meant a lot to me.I consider myself privileged to have both of you as my supervisors.I am grateful to the members of the assessment committee, namely Prof.

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)0.011
metaresearch head score (Gemma)0.024
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: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.017
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0040.005
Scholarly communication0.0140.005
Open science0.0010.009
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0160.002

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 head0.020
GPT teacher head0.328
Teacher spread0.308 · 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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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