Place Marketing, Place Branding, and Social Media: Perspectives of Municipal Practitioners
Bibliographic record
Abstract
Abstract The purpose of this paper is to examine how social media is used as a promotional tool for economic development at the municipal level through place marketing and branding, to identify and explore what specific tools are being used, and to discuss the strengths and limitations of use. Primary data was collected through in‐depth interviews with 16 municipal economic development practitioners in the Province of Ontario, Canada. Additional data collected from municipal social media accounts was used to further reinforce the qualitative data and allow for triangulation and greater breadth of analysis. The findings suggest that social media is being used as a tool to promote local services and political information rather than communicate a brand position. Additionally, limited interaction with the audience reduces not only the effectiveness of place marketing efforts but also enhancement of place brands. While some findings are place specific (as study design limited research participants to municipalities in Ontario), these findings can be shared with other locales in advanced economies to provide them with information to make improvements in the way communication technology is utilised. This research paper provides a better understanding of how social media is being used by municipalities and how it fits into place marketing and place branding.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 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".