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Record W2301053704

Critical ethnographic research of literacy practices used in youth community-based organisations in Québec : what ethical concerns?

2013· article· en· W2301053704 on OpenAlexaboutno aff
Virginie Thériault

Bibliographic record

VenueLancaster EPrints (Lancaster University) · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicQualitative Research Methods and Ethics
Canadian institutionsnot available
Fundersnot available
KeywordsDilemmaHarmEthical dilemmaSociologyEthnographyConfidentialityPower (physics)Research ethicsQualitative researchInformed consentPublic relationsSocial psychologyPsychologyPolitical scienceSocial scienceLawMedicine
DOInot available

Abstract

fetched live from OpenAlex

General ethical principles such as informed consent, confidentiality, participants’ physical or psychological well-being and the absence of preventable harm should be respected in any research project. However, those guidelines cannot help researchers in specific situations, when they face a difficult situation in the fieldsite: this is what is called an ‘ethical dilemma’ (Goodwin, et al., 2003) or ‘everyday ethics’ (Guillemin & Heggen, 2009). However, what are the ethical implications of doing qualitative research with vulnerable groups of people considering that they are “potentially vulnerable on a variety of levels, because of their marginalized social, political and economic position.” (Laverick, 2010, p.76)? Moreover, the voices of vulnerable or marginalized groups are generally not heard in society and often they have little power in the research process (Pyett, 2002). In this presentation, I will discuss my own experience as a young researcher doing a critical ethnographic study in two organisations working with young people with low levels of formal education in Québec (Canada). At each site, I conducted regular observations over a period of two months, focusing on group activities. During that period, I experienced various ethical dilemmas that challenged my role and position towards the participants. In my talk, I will expose some of the ethical dilemmas encountered in the two fieldsites. I will explain how I dealt with them and what their implications for my study are.

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)0.012
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity, Insufficient 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 score0.172
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.003
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.004
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 head0.515
GPT teacher head0.558
Teacher spread0.043 · 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
Published2013
Admission routes1
Has abstractyes

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