SMART CITIES, SMART TOURISM? THE CASE OF THE CITY OF PORTO
Bibliographic record
Abstract
The so-called Smart Cities have been playing an important role in the academic literatureas well as in the agenda of public policies. With the forward thinking of “creating” newurban development models, the cities intend to strategically positioning themselves and atthe same time develop cooperation networks. Frequently using the Information andCommunication Technologies (while as a means to an end and not as an end itself), thecities try to assure a greater economic competitiveness, the environmental sustainabilityand the reinforcement of citizenship (calling out to people to participate, in the scope of aninclusive logic and in an appeal to creativity and social responsibility).Even the 2020European strategy promotes this development.The geographic scope of the study islimited to the city of Porto. This is justified by the fact that, in 2015, Porto was one the fiveEuropean cities selected to participate in the GrowSmarter (model of organization of citiesof the future), an ambitious project with the objective of making Europe more sustainableand environmentally intelligent.Besides that, the Portuguese Smart Cities Index, 2016, points out the city of Oporto as thePortuguese city with better results in what concerns the main vectors of intelligence(policy, strategies and projects implemented, edification, mobility, energy and smartservices).At the same time, in the international press, the city of Porto appears as areference for its architectural wealth and as one of the more indicated Europeandestinations for one who would like to enjoy quality holidays at a reasonable price.That isperhaps why Shermans Travel presents Porto as one of the top 10 destinations forintelligent tourists. The purpose of the present study is to understand if the emergence ofsmart cities can be in some way connected to the appearance of smart tourism. For such apurpose, we will study the city of Porto and a survey will be conducted among the touristsof the city.This article compiles some of the first results of the study, referring to the datacollected during the months of August and September of 2017, and intends mainly to setout some hypotheses about the motivation for the choice of a tourist destination, that willbe developed in future works.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| 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".