Time and time again, the residents of Los Angeles have used the promises of the “garden city” and urban agriculture to imagine a way out of the persistent problems of urban life. In the contemporary moment, alternative food system activists are again working to create a cultural movement committed to “slow,” local and small-scale community-based urban food systems – which, paradoxically, they are organizing through sophisticated digital tools and global social media. This study argues that, beyond debates over urban diets, food is a lens through which post-industrial imaginaries of an egalitarian digital society are fused with pre-industrial imaginaries of utopian, agrarian communities. The consequence of these entangled visions is a unique set of practices that attempt to inject a sense of radical possibility into an urban geography that is itself the result of decades of struggle over land use, livelihoods, and urban culture. Drawing on a combination of archival research, ethnography, interviews and a survey, this dissertation examines how planners, boosters, government officials, and ordinary citizens have sought to wield food and agriculture in the city as a force to ameliorate the economic, social and ecological alienation that can dominate urban life. This study shows that the political, class and racial underpinnings of urban food movements are far more complex and contingent than normally understood, and the case of Los Angeles reveals both deeply conservative, reactionary moments and unprecedented coalitions emerging to make claims on the right to self determination, health and progressive social change in the city.

Many fabrication methods for scaffolds made with biodegradable polymers and hydrogels have shown promise for tissue engineering applications. Both possess their own benefits and disadvantages. However, by utilizing both, the advantages of each can be combined into a single hybrid scaffold. By increasing the reaction concentration of a modified synthesis protocol previously published, the lower critical solution temperature (LCST) of a biodegradable, thermos-sensitive hydrogel, poly (ethylene glycol)-poly (N-isopropylacrylamide) (PEG-PNIPAAm), was optimized to its lowest possible concentration for gelation (5 wt. %). The resulting PEG-PNIPAAm was blended with a hydrophobic, biodegradable polymer, poly (ε-caprolactone) (PCL), and electrospun into a 3-dimensional (3D), electrospun hybrid scaffold. The blending ratio of PEG-PNIPAAm:PCL (13:9), scaffold porosity (90%) and fiber diameter (11 μm) where optimized for uniform cell seeding and cell encapsulation within the dissolved hydrogel. During cell seeding, PEG-PNIPAAm dissolved into the media solution, surrounding seeded cells and filling the scaffolds pore volume without the need of a mold. When incubated at 37 °C, the seeded cells are encapsulated within the PEG-PNIPAAm hydrogel. The hydrophobic PCL network remains as a structural support, increasing the compressive, elastic and viscoelastic properties of PEG-PNIPAAm. Including PEG-PNIPAAm within the PCL scaffold, also resulted in the improvement of mechanical characteristics over PCL control scaffolds. Culturing mesenchymal stem cells (MSCs) within hybrid scaffolds for two weeks under chondrogenic conditions increased chondrogenic gene expression and proteoglycan deposition compared to PCL controls with a similar scaffold porosity and fiber diameters. The addition of PEG-PNIPAAm within a 3D structured, PCL network produces a hybrid scaffold with the potential for a variety of tissue engineering applications.

There has been much recent research in corpus and computational linguistics on distributional learning algorithms—computer code that induces latent linguistic structures in corpus data based on co-occurrences of transcribed units in that data. These algorithms have varied applications, from the investigation of human cognitive processes to the corpus extraction of relevant linguistic structures for lexicographic, second language learning, or natural language processing applications, among others. They also operate at various levels of linguistic structure, from phonetics to syntax.

One area of research on distributional learning algorithms in which there remains relatively little work is the learning of multi-word, memorized, formulaic sequences, based on the co-occurrences of words. Examples of such multi-word expressions (MWEs) include kick the bucket, New York City, sit down, and as a matter of fact. In this dissertation, I present a novel computational approach to the distributional learning of such sequences in corpora. Entitled MERGE (Multi-word Expressions from the Recursive Grouping of Elements), my algorithm iteratively works by (1) assigning a statistical ‘attraction’ score to each two-word sequence (bigram) in a corpus, based on the individual and co-occurrence frequencies of these two words in that corpus; and (2) merging the highest-scoring bigram into a single, lexicalized unit. These two steps then repeat until some maximum number of iterations or minimum score threshold is reached (since, broadly speaking, the winning score progressively decreases with increasing iterations). Because one (or both) of the ‘words’ making up a winning bigram may be an output merged item from a previous iteration, the algorithm is able to learn MWEs that are in principle of any length (e.g., apple pie versus I’ll believe it when I see it). Moreover, these MWEs may contain one or more discontinuities of different sizes, up to some maximum size threshold (measured in words) specified by the user (e.g., as _ as in as tall as and as big as). Typically, the extraction of MWEs has been handled by algorithms that identify only continuous sequences, and in which the user must specify the length(s) of the sequences to be extracted beforehand; thus, MERGE offers a bottom-up, distributional-based approach that addresses these issues.

In the present dissertation, in addition to describing the algorithm, I report three rating experiments and one corpus-based early child language study that validate the efficacy of MERGE in identifying MWEs. In one experiment, participants rate sequences extracted from a corpus by the algorithm for how well they instantiate true MWEs. As expected, the results reveal that the high-scoring output items that MERGE identifies early in its iterative process are rated as ‘good’ MWEs by participants (based on certain subjective criteria), with the quality of these ratings decreasing for output from later iterations (i.e., output items that were scored lower by the algorithm).

In the other two experiments, participants rate high-ranking output both from MERGE and from an existing algorithm from the literature that also learns MWEs of various lengths—the Adjusted Frequency List (Brook O’Donnell 2011). Comparison of participant ratings reveals that the items that MERGE acquires are rated more highly than those acquired by the Adjusted Frequency List, suggesting that MERGE is a performance frontrunner among distributional learning algorithms of MWEs. More broadly, together the experiments suggest that MERGE acquires representations that are compatible with adult knowledge of formulaic language, and thus it may be useful for any number of research applications that rely on such formulaic language as a unit of analysis.

Finally, in a study using two corpora of caregiver-child interactions, I run MERGE on caregiver utterances and then show that, of the MWEs induced by the algorithm, those that go on to be later acquired by the children receive higher scores by the algorithm than those that do not go on to be learned. These results suggest that, when applied to acquisition data, the algorithm is useful for identifying the structures of statistical co-occurrences in the caregiver input that are relevant to children in their acquisition of early multi-word knowledge.

Overall, MERGE is shown to be a powerful computational approach to the distributional learning and extraction of MWEs, both when modeling adult knowledge of formulaic language, and when accounting for the early multi-word structures acquired by children.