Crafting Authenticity: How Heritage Shop Owners Shape the Consumer Subject
Bibliographic record
Abstract
From the mid-1980s through the early-2020s, Montreal’s commercial landscape saw a proliferation of heritage shops selling imported items from South-Asia such as incense, crystals, clothing, jewelry, home décor, and more. Today, the city’s zeitgeist has changed, but these shops still remain. Hence, what efforts do these shop owners exert to construct their authenticity and keep this heritage consumer subject alive? Previous research has either taken too vast of an institutional approach in studying this phenomenon or has focused too closely on the product that is the consumer subject itself. Thus, the literature does not take a balanced approach by studying the retail market actors themselves and the actions they specifically take to shape their ideal consumer subject. To fill this gap, this research employs a qualitative approach, combining three research methods: in-depth interviews, fieldwork, and secondary data. Overall, this thesis uncovers the muti-layered process that heritage shop owners undertake to shape consumer subjects. It contributes to the marketing literature by applying existing notions of authenticity to a new commercial niche and geographical context and by investigating the role that shop owners specifically play in the ethnic market sphere. For managers, this study can serve as a beginner’s guide for opening a business—or filling in any gaps of an existing business— that employs the construct of authenticity as understood, perceived, and crafted through managers’ very own eyes.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.028 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".