Advances in Cultural Heritage Studies : Year 2020 : Contributions of the European Students’ Association for Cultural Heritage
Bibliographic record
Abstract
The announcement of the creation of a European Year of Cultural Heritage (year 2018) – by the Decision 2017/864 of the European Parliament – encouraged the creation, in 2017, of the European Students’ Association for Cultural Heritage (ESACH). ESACH has become the first still-growing interdisciplinary and cross- -generational network in the field. Currently brings together young researchers and researchers at early stages of their careers, in the fields of culture and heritage, from all kinds of academic disciplines and is made up of members from various European universities and research centres (see www.esach.org). Within the network, the main questions are: How do we engage with the past elements of our culture(s)? How and why do we protect culture as a genuine element of a contemporary cultural system? What do younger generations state as heritage and what ways do they see to safeguard and experience it? ESACH stands up for a participatory way of involvement and is eager to take part in the cultural discourse at European and national levels. Since ESACHS’ foundation, the Portuguese publisher Mazu Press (www.mazupress.com) has been associated with the initiatives of the Portuguese branch of ESACH based in Lisbon (Sharing Heritage Lisbon), firstly with the promotion actions and then with the publication of the book “New Perspectives in Interdisciplinary Cultural Heritage Studies. Contributions of the European Students’ Association for Cultural Heritage in the European Year of Cultural Heritage 2018”. In this atypical Covid-19 pandemic year, Mazu Press again invited ESACH to join the renewed idea of “unifying through Cultural Heritage”, creating the opportunity for all to associate their efforts to this volume of “Advances in Cultural Heritage Studies, Year 2020”. Until now, ESACH members have been given the opportunity to contribute their ideas in several European events organized by the respective stakeholders, such as the Genoa Meeting, in October 2019, which had the cultural, logistic and financial support of the University of Genoa and foremost the PhD Course in Study and Enhancement of the Historical, Artistic-Architectural and Environmental Heritage. This book brings together twenty chapters by twenty four authors from Canada, Greece, Ireland, Italy, Poland, Portugal, Slovakia, Spain and Turkey. This sharing of knowledge, culture and heritage studies, through various disciplines, shows the richness – advances and new perspectives – generated by the common passion for cultural heritage.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".