Notice bibliographique
Résumé
This report critically examines, for the first time, the capacity of Canada’s water sector with respect to meeting and helping other countries meet the water-related targets of the UN’s global sustainable development agenda. Several components of this capacity are examined, including water education and research, investment in water projects that Canada makes internally and externally, and experiences in water technology and governance. Analysis of the water education system suggests that there is a broad capability in institutions of higher learning in Canada to offer training in the diverse subject areas important in water. In most cases, however, this has not led to the establishment of specific water study programmes. Only a few universities provide integrated water education. There is a need for a comprehensive listing of water-related educational activities in universities and colleges — a useful resource for potential students and employers. A review of recent Canadian water research directions and highlights reveals strong and diverse water research capacity and placed the country among global leaders in this field. Canada appears to be within the top 10 countries in terms of water research productivity (publications) and research impact (citations). Research capacity has been traditionally strong in the restoration and protection of the lakes, prediction of changes in climate, water and cryosphere (areas where water is in solid forms such as ice and snow), prediction and management of floods and droughts. There is also a range of other strong water research directions. Canada is not among the top 10 global water aid donors in absolute dollar numbers; the forerunners are, as a rule, the countries with higher GDP per capita. Canadian investments in Africa water development were consistently higher over the years than investments in other regions of the global South. The contributions dropped significantly in recent years overall, also with a decline in aid flow to Africa. Given government support for the right business model and access to resources, there is significant capacity within the Canadian water sector to deliver water technology projects with effective sustainable outcomes for the developing world. The report recommends several potential avenues to elevate Canada’s role on the global water stage, i.e. innovative, diverse and specific approaches such as developing a national inventory of available water professional capacity, and ranking Universities on the strength of their water programmes coordinating national contributions to global sustainability processes around the largest ever university-led water research programme in the world – the 7-year Global Water Futures program targeting specific developmental or regional challenges through overseas development aid to achieve quick wins that may require only modest investments resolving such chronic internal water challenges as water supply and sanitation of First Nations, and illustrating how this can be achieved within a limited period with good will strengthening and expanding links with UN-Water and other UN organisations involved in global water policy work To improve water management at home, and to promote water Canadian competence abroad, the diverse efforts of the country’s water sector need better coordination. There is a significant role for government at all levels, but especially federally, in this process.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,009 | 0,026 |
| Études des sciences et des technologies | 0,006 | 0,004 |
| Communication savante | 0,010 | 0,004 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».