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Record W4280568100 · doi:10.36367/ntqr.10.2022.e514

Conducting international online surveys: Trials, tribulations, and suggestions for success

2022· book· en· W4280568100 on OpenAlexaff
Margareth Santos Zanchetta, Kateryna Metersky, Marcelo Medeiros, Walterlânia Silva Santos, Christian Mésenge

Bibliographic record

VenueNew Trends in Qualitative Research · 2022
Typebook
Languageen
FieldSocial Sciences
TopicSurvey Methodology and Nonresponse
Canadian institutionsFrancophone University AssociationToronto Metropolitan University
FundersInvention for Innovation
KeywordsPsychology

Abstract

fetched live from OpenAlex

Framework- In the context of the need for the production of knowledge in low- and middle-income countries, as well as in high-income countries with their socially vulnerable populations and the concomitant, minimal availability of funding for international research, university researchers should innovate. Goal- Discuss critical methodological issues in the process of designing and implementing international online survey research. This is done in the context of responding to the need for innovation in data collection tools to expand their responsiveness to the international field and the participants’ characteristics. Chapter organization- The chapter is organized with the presentation of online international research, first presenting insights for an alternative and innovative design, then formulating questions to remotely collect international data, renewing a dialogue setting, and exploring issues of recruitment, attrition and participation. It also reports successful experiences of the internationalization of research, intellectual partnerships and shared successes in the process of creating, exchanging and translating knowledge in the context of global health and the democratization of knowledge. The experiences are related to qualitative inspired research implemented in the continental sphere (Africa, South and North America, and Europe) with the creation of survey questionnaires for an exploration of narratives, experiences, and decisions. Final consideration- The mobilization of researchers’ social and professional networks, in addition to the constant reformulation of intellectual partnerships in research, are today the most common strategies to face the current challenges in academia. Innovation for methodological advances in audacious design for unpredictable fieldwork may require the revisiting of epistemological grounds. Emerging issues in this type of research, such as “research fatigue”, should be considered.

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)0.875
metaresearch head score (Gemma)0.901
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.125
Threshold uncertainty score0.154

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.8750.901
Meta-epidemiology (narrow)0.0050.003
Meta-epidemiology (broad)0.0070.006
Bibliometrics0.0100.017
Science and technology studies0.0130.030
Scholarly communication0.0300.044
Open science0.0130.020
Research integrity0.0140.014
Insufficient payload (model declined to judge)0.0120.004

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 head0.924
GPT teacher head0.737
Teacher spread0.187 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
DomainMethods
GenreMethods

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

Citations4
Published2022
Admission routes1
Has abstractyes

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