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Concept maps have been described as a valuable tool for exploring curriculum knowledge. However, less attention has been given to the use of them to visualise contested and tacit knowledge, i.e. the values and perceptions of teachers that underpin their practice. This paper aims to explore the use of concept mapping to uncover academics’ views and help them articulate their perspectives within the framework provided by the concepts of pedagogic frailty and resilience in a collaborative environment.

Participants were a group of five colleagues within a Biochemical Science Department, working on the development of a new undergraduate curriculum. A qualitative single-case study was conducted to get some insights on how concept mapping might scaffold each step of the collaborative process. They answered the online questionnaire; their answers were “translated” into an initial expert-constructed concept map, which was offered as a starting point to articulate their views during a group session, resulting in a consensus map.

Engaging with the questionnaire was useful for providing the participants with an example of an “excellent” map, sensitising them to the core concepts and the possible links between them, without imposing a high level of cognitive load. This fostered dialogue of complex ideas, introducing the potential benefits of consensus maps in team-based projects.

An online questionnaire may facilitate the application of the pedagogic frailty model for academic development by scaling up the mapping process. The map-mediated facilitation of dialogue within teams of academics may facilitate faculty development by making explicit the underpinning values held by team members.

The development of models that allows the evaluation and prediction of erosion processes is an important tool for the management and planning of coastal systems. Mangrove forests systems are under threat by the impacts of erosion, which is also intensified by human activity (and aggravated in the scenarios of global warming and climate change). The purpose of this paper is to develop a model of geographic information systems (GIS) that can be used for any estuary area, but it can also be used for mangroves.

This paper uses georeferentiation which is defined as a set of parameters that best characterize the mangrove areas: elevation (m); geomorphology; geology; land cover; anthropogenic activities; distance to the coastline (m) and maximum tidal range (m). Three different methods are used to combine the various vulnerability parameters, namely, DRASTIC index, analytical hierarchy process (AHP) and square root of the geometric mean.

The three approaches presented in this work show different types evaluating vulnerability to erosion, highlighting a stronger overvaluation of the areas presented with a high vulnerability, through the use of DRASTIC index when compared with two other approaches. The use of the AHP shows similarity to the square root of the geometric mean model, but the AHP also presents a higher percentage of vulnerable areas classified as having medium to very high vulnerability. On the other hand, the use of square root of the geometric mean led to a higher percentage of areas classified as having low and very low vulnerability.

These three qualitative models, based on a cognitive approach, using the set of parameters defined in this research, are a good tool for the spatial distribution of erosion in different mangroves in the world.

Global warming and climate change scenarios require adaptation and mitigation options supported by science-based strategies and solutions.

User-generated social media comments can be a useful source of information for understanding online corporate reputation. However, the manual classification of these comments is challenging due to their high volume and unstructured nature. The purpose of this paper is to develop a classification framework and machine learning model to overcome these limitations.

The authors create a multi-dimensional classification framework for the online corporate reputation that includes six main dimensions synthesized from prior literature: quality, reliability, responsibility, successfulness, pleasantness and innovativeness. To evaluate the classification framework’s performance on real data, the authors retrieve 19,991 social media comments about two Finnish banks and use a convolutional neural network (CNN) to classify automatically the comments based on manually annotated training data.

After parameter optimization, the neural network achieves an accuracy between 52.7 and 65.2 percent on real-world data, which is reasonable given the high number of classes. The findings also indicate that prior work has not captured all the facets of online corporate reputation.

For practical purposes, the authors provide a comprehensive classification framework for online corporate reputation, which companies and organizations operating in various domains can use. Moreover, the authors demonstrate that using a limited amount of training data can yield a satisfactory multiclass classifier when using CNN.

This is the first attempt at automatically classifying online corporate reputation using an online-specific classification framework.