Foundations are much more than disinterested philanthropic institutions that award grants to service-providing nonprofits. Foundations are political actors that seek to produce social change, not only by donating resources to nonprofits that promote causes but also by supporting policy reform in a more direct manner. We investigate engagement in advocacy among community foundations in the USA, which we define as the effort to influence public policy by proposing or endorsing ideas and by mobilizing stakeholders for social change. Drawing primarily on organizational sociology, we posit that the environmental context in which community foundations are situated and particular structural characteristics or operational features of community foundations (institutional logics, identity and embeddedness, and managerialism) will be associated with advocacy. We utilize machine learning techniques to establish an outcome measure of advocacy discourse on community foundation websites and ordinary least squares regression to model that outcome with a cross-sectional dataset compiled from multiple sources. We find considerable support for our conceptual frame, and we conclude by offering an agenda for future research on foundations as interest groups.
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Since some sentences (such as titles) did not have a period at the end, we decided to use new line (“\n”), horizontal tab (“\t”), and carriage return (“\r”) markers as delimiters, in addition to periods.
The random baseline (the probability of correctly classifying a sentence at random) was 45%. Higher precision than recall meant that our classifier was conservative.
We have data on whether or not community foundations commissioned independent audits, another common indicator of rationalization. We did not include the indicator because it was not statistically significant in any of our models and was less informative than the index we created about consulting.
All models are Ordinary Least Squares (OLS) regressions. In order for these models to produce unbiased estimates, 6 main assumptions need to be met: (1) the outcome should be the function of additive parameters, (2) the observations used for estimation should be representative of the population of interest, (3) the predictive errors should have mean zero, (4) none of the predictive variables should be perfectly collinear, (5) the variance of the predictive errors should be constant (homoscedastic errors), (6) the predictive errors should be normally distributed. We believe that in our case these assumptions are met: (a) we theoretically envision the covariates of interest to have an additive effect on the outcome, (b) we collected data for a large and representative number of community foundations, (c) the predictive errors have mean zero, (d) none of the variables are collinear (we never include variables representing the same dimension in the same model—e.g. Donative Revenue and Donative Dominance--), (e) the models generate homoscedastic errors, and (f) the distribution of the predictive errors follow a quasi-normal distribution (for some of the models the distribution is slightly right skewed).
In addition to the controls presented in the table, in preliminary models we tested a variety of alternative and additional controls. Since none of them changed the main results or added meaningful explanatory power, we decided to present parsimonious models.
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The authors would like to thank the two anonymous reviewers, Jeffrey Berry, Christof Brandtner, Patricia Bromley, Sarah Chasins, Emily Finchum-Mason, Aaron Horvath, and Walter Powell for their constructive comments. Prior versions of this paper were presented at the Scandinavian Consortium for Organizational Research (SCANCOR) at Stanford University and at the ARNOVA Annual Conference in Grand Rapids, MI. This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2016S1A3A2925085).
We provide here the list of the terms we used to identify, among all 1,444,226 sentences, advocacy discourse. Note that in order to perform this task, these terms and all words in the complete corpus of sentences were stemmed using a Porter stemmer. As an example, the first stem “advoc” allowed us to find sentences that had the word advocacy but also the word advocate in them.
We report here the cross-validated accuracy of different pre-processing configurations and machine learning algorithms that motivated our final selections. We tested three different pre-processing configurations and four machine learning models.
|Remove punctuation: delete punctuation signs.||YES||YES||YES|
|Remove numbers: delete numbers present in the text||YES||YES||YES|
|Remove stopwords: delete common words such “I” and “we”||YES||YES||YES|
|Make all text lowercase||Upper||Lower||Lower|
|Stemming: keep only the stem-root of words (Porter stemmer)||NO||NO||YES|
Machine learning models
NB (Naive Bayes).
SVM (Support Vector Machine).
ENSEMBLE: Logistic Regression using NB and SVM predicted probabilities as features.
DOC2VEC: Logistic Regression using document embeddings as features. Document embeddings of length 100 were predicted using a Doc2Vec model (Le and Mikolov 2014) trained (300 forward and backward propagation iterations) on our labels.
In Fig. 2, we report precision and recall measures calculated using 100-fold cross-validation and a 70–30 train-test split. The three subfigures in the top row report the results for the NB model, the three subfigures in the second row report the results for the SVM model, the results for the Ensemble are in the third row and the ones for the Doc2Vec model are in the bottom row. The subfigures on the left contain results when using the 1st pre-processing configuration, the subfigures in the middle show the results when using the second configuration, and the subfigure on the right provide accuracy measures when using the third configuration. Independently of the pre-processing set up, all models were trained using unigram, bigram, and trigram features from the remaining text. We find that the model with the highest predictive accuracy is the Ensemble model trained with features coming from the second pre-processing configuration. We get very satisfactory results for that model and configuration, 83% precision and 70% recall. These precision and recall percentages are based on predictions made by a logistic regression using a 50% probability threshold: sentences with a predicted likelihood higher than 50% are classified as being about advocacy. As can be seen in Fig. 3, by increasing this probability threshold from 50 to 60% during testing we reduced recall but we were able to increase the model precision to 87%. Since our main concern was to be as certain as possible about the predicted true positives, we opted for a 60% probability threshold during classification.
Appendix 3: Correlation table
|Comm wealth||Political liberalism||Community logic||Donation||Donative Dom||Embedded||Social Justice||Profession||Rational||Age||Assets|
|Professionalization||.03||.02||− .04||− .12||− .11||− .02||.09||1|
|Age||.04||.10||.17||− .21||− .15||.25||.06||− .07||.21||1|
|Assets||.07||.23||.32||− .06||.02||.57||.22||− .26||.53||.44||1|
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Suárez, D.F., Husted, K. & Casas, A. Community foundations as advocates: social change discourse in the philanthropic sector. Int Groups Adv 7, 206–232 (2018). https://doi.org/10.1057/s41309-018-0039-z