A Cross-Cultural Study: Sociological Appropriation of Short Message Service (SMS) A Taiwanese Canadian Experience
Bibliographic record
Abstract
After five years of study here at Simon Fraser University, I would like to extend my heart felt thanks to the School of Communication for endowing me with a new set of perspectives in examining the world around me.Without the comprehensive training in reviewing literature as well as the instilling of a set of critical thinking and objective researching skills, the completion of this honours thesis would not have been possible The completion of my thesis would not have been possible without the continuous support of my senior supervisor, I am truly grateful for the wonderful experience, insights and continuous encouragements that Dr. Richard Smith has poured into my study.His exceptional knowledge and expertise in science and technology in the realm of communication studies was one of the original reasons that prompted my initial interest in exploring the field of human research in greater lengths.As well, I would also like to thank Roman Onufrijchuk in supporting me as my secondary supervisor.I am also extremely appreciative to my peers at Simon Fraser University who have support me as well as my friends in both Taiwan and Canada who have made this project possible.Inspiration carries a lot of weight, but it takes motivation and courage to face difficult challenges, I have learned these crucial lessons through the dedication in this project.Above all, the past five years have been a remarkable time in my life, which has provided me with the foundation to pursue further studies in academia.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.023 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".