INTAROS Community-Based Monitoring Experience Exchange Workshop Report, Québec City, Québec , December 11 to 12, 2017.
Bibliographic record
Abstract
This INTAROS Community-Based Monitoring Workshop was held in Québec at the Québec Convention Centre on December 11-12, 2017 concurrently with the Arctic Change 2017 Conference. The workshop offered an opportunity for practitioners of community-based monitoring (CBM) and observing programs from northern Canada to come together to exchange experiences and perspectives. Representatives of ten CBM programs attended; additional participants included representatives of co-management boards, northern research institutions, Inuit organizations, philanthropic organizations, and programs focused on developing or adapting tools for data management and sharing. The objective of the Quebec workshop was to facilitate exchange of ideas and information among CBM practitioners from Canada. An agenda for the workshop was developed based on input from participants. The agenda included time for brief presentations from CBM programs, breakout and plenary discussion groups, and time for networking over meals and games. The conclusions of the discussions at the workshop are summarized below. The motivations for implementing CBM programs differ but included: influencing decisions about industrial development and regulations in fishing and hunting; gaining a better understanding of the challenges and opportunities of climate change and social and human health conditions, as well as education and capacity building. Similarly, the motivation for individuals to be involved in CBM varied but included addressing the practical needs of communities. Other sources of motivation for individuals included developing a better understanding the environment, and sharing knowledge and learning from each other. There were a variety of attributes being monitored by the CBM programs in attendance, although there were still many information needs and gaps identified. A variety of people and organizations are using CBM generated information including: individuals, hunter trapper organizations, civil society organisations, industry, and government organizations at all levels, especially wildlife management agencies. Good practices are considered practices that have proven to work well for CBM programs. These included CBM practices that are supported by the community, provide capacity building opportunities, link Traditional Knowledge (TK) and science, and document TK. Trust among community members and scientists is also important. Challenges that CBM program representatives have faced included the ability to secure long term funding leading to gaps in data records over time. Other challenges included reconciling science and community priorities, linking quantitative with qualitative approaches, and meaningful dissemination of information. There were also challenges related to avoiding misconceptions of how the data can be used, timeliness of producing accessible data, community burnout, and difficulties of growing a program. Other challenges included a lack of technical support, limitations in community infrastructure and connectivity, and difficulties in influencing change. There was also a general agreement that CBM programs need to evolve, building on what we have learned rather than doing things the way they have always been done. In terms of sustainability of CBM, it was concluded that CBM sustainability can be enhanced through partnerships and working together. This could lead to shared data platforms and better coordinated efforts to reduce redundancy. CBM programs that are able to be relevant and address the needs of communities, scientists and decision makers are more likely to be sustained. With regards to contributing to decision making, it is important for CBM information to be included in decisions about industrial development. Decision makers often need to understand large scale processes. For CBM data to contribute, it needs to be interoperable (able to be analyzed across different programs). This is sometimes difficult since CBM programs and community priorities vary. With regards to data and data collection in CBM programs, methods of data collection must be culturally appropriate. Community consultation to create data sharing agreements should happen before a project is implemented. All parties need to be clear on what happens to data after it is collected. The community should have the opportunity to verify the data and decide what to make publicly available. CBM organizers need to take into account the connectivity and infrastructure of rural communities. Data and information needs to be returned to communities, not just in summary form, but also the raw data. A repository of data should be available to community members to meet current and future information needs. The technical challenges to data sharing are not as great as the jurisdictional and political challenges to data sharing. Successful CBM programs build on mutual respect and understanding, which comes from listening and educating oneself. Certain people are talented at building bridges between science and Arctic communities. CBM programs ought to hire and support these individuals. It is important to consider the implications of the CBM program on Indigenous rights. Participants recognized that working together will improve long term success of CBM. Benefits of a network could include many aspects. It could help researchers from outside the community understand where the gaps are in what is being monitored and avoid duplication of efforts. A network could contribute to better employment and training and capacity building opportunities (e.g. could potentially provide small grants to facilitate skill building and knowledge exchange of CBM programs). It could facilitate exchange of information to learn from the mistakes and successes of others, in addition to better understanding how other communities have successfully dealt with change. A network could advocate for CBM to be valued in decision making, risk management, and economic development, and for changes to funding structures. A CBM network would need to be flexible, as communities are diverse. It is important to provide benefits to network participants, and recognize that participation may vary over time. The report concludes with a number of suggested good practices and needs for CBM and observing programs in northern Canada.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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; both teacher heads agree on what is shown here.
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".