Cultural Cartography: Pathways to Enhance Netnographic Integration
Bibliographic record
Abstract
This research introduces cultural cartography as an innovative methodological approach integrating ontological foundations with reflective praxis to deepen digital discourse analysis. Using the 2022 Rogers Communications internet outage in Canada as a case study, we demonstrate the process of effective map-making and apply Narrative Mosaic Analysis (NMA) to examine how social media users expressed their frustrations, humour, and calls for accountability during the crisis. By mapping and analyzing textual and symbolic elements like emojis, memes, and hashtags, this approach uncovers how communities construct and communicate their shared experiences online. Cultural cartography advances netnographic research by offering a robust framework for integrating the nuanced dynamics of digital discourse, enabling a more comprehensive interpretation of how cultural realities are articulated and shared within digital environments. This methodological innovation provides a new lens for exploring the symbolic richness of online interactions and the narratives they create.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.018 | 0.023 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".